Image_Enhancement
-
AeroLLE: Constrained Pseudo-Supervision for Nighttime Aerial Image Enhancement with the AeroNight-1.5K Benchmark
2026-08-01 15:02:28Wei Lu, Hongyuan Liu, Si-Bao ChenAbstract
Nighttime aerial image enhancement is challenged by spatially nonuniform exposure, mixed illumination, and weak structural evidence, while registered normal-light targets are difficult to capture from moving platforms. Generated normal-light images provide practical appearance guidance but may alter geometry or texture. We introduce \aeronight{}, comprising 1,500 real nighttime aerial RGB images: 1,300 inputs are associated with manually screened pseudo-references, and 200 inputs support unpaired evaluation. We propose AeroLLE, a two-stage framework that first recovers visibility with an HVI Base Enhancer and then performs Spatially Adaptive Exposure--Color Calibration (SAECC). After the Base Enhancer is selected and frozen, SAECC predicts bounded, low-resolution RGB gain and bias fields, restricting the magnitude and spatial variation of the second-stage correction. Experiments under complementary pseudo-paired and unpaired protocols demonstrate improved agreement with screened appearance targets, together with more balanced exposure and color correction across diverse nighttime aerial scenes. These results support constrained, stage-specific calibration as a practical strategy for learning from generated appearance guidance when registered aerial references are unavailable.
Abstract (translated)
URL
https://arxiv.org/abs/2608.00702
PDF
-
RPL-UIE: Reliable Prior Learning for Underwater Image Enhancement
2026-07-31 14:30:36Yifan Chen, Jiaming Liu, Ye Zheng, Zhe Sun, Tao ChenAbstract
Underwater image enhancement (UIE) aims to recover clear images from observations affected by wavelength-dependent absorption, scattering, and spatially nonuniform degradation. Although existing generative methods can handle complex degradations, severe information loss may lead to semantic drift in the restored results. To address this issue, we propose RPL-UIE, a two-stage teacher--student framework for reliable prior learning. In the teacher stage, the network learns reliable and complementary spatial priors characterizing appearance and photometric properties from paired degraded and reference images. In the student stage, the network takes only degraded images as input and learns to emulate the teacher's prior extraction capability, thereby providing more reliable restoration guidance for the enhancement process without requiring reference images at inference. To reduce the prior-learning discrepancy between the teacher and student models, we further develop Residual Prior Refinement Diffusion (RPRD) and Frequency-Aware Prior Residual Calibration (FPRC). RPRD uses the coarse priors as anchors and progressively predicts the necessary corrections in the residual space. FPRC retains stable low-frequency residual components and selectively modulates high-frequency detail residuals, producing calibrated priors to support high-quality reconstruction. Experiments on multiple UIE benchmarks demonstrate competitive restoration performance. Downstream underwater object detection and instance segmentation experiments further demonstrate the improved utility of enhanced images for visual perception, while tests on real-world data captured by a remotely operated vehicle (ROV) support the practical applicability of RPL-UIE.
Abstract (translated)
URL
https://arxiv.org/abs/2608.00137
PDF
-
Learning Color Grading, No Photo Sharing: Federated Aesthetic Preference Learning for Personalized Image Enhancement
2026-07-30 04:16:38Chuanzhi Xu, Ziyuan Tao, Jean Julien KNell, Yanrong Chen, Haolan Guo, Xuanhua Yin, Adnan Mahmood, Weidong CaiAbstract
Personalized image enhancement should reflect individual aesthetic taste, yet learning such preferences commonly depends on private photos and ratings that are unsuitable for centralized collection. The task must infer preference from sparse, heterogeneous feedback and translate it into natural-looking color transformations on resource-constrained user devices. We introduce FedPAIE, a federated personalized aesthetic image enhancement framework for user-adaptive color grading without centralizing raw photos or ratings. FedPAIE trains a lightweight dual-cue aesthetic scorer, calibrates it into a personalized scorer on a small local support set, and freezes it to guide regularized adaptation of a lightweight CLUT enhancer from unpaired local photographs. Fidelity constraints and an excess-gap penalty regularize scorer-guided adaptation to limit proxy-score over-optimization while preserving content and natural appearance. Training remains lightweight throughout the pipeline: scorer learning updates at most 0.787M parameters, enhancer adaptation updates 0.265M, and inference retains only a 0.293M-parameter personalized enhancer. Experiments on MIT-Adobe FiveK and Flickr-AES demonstrate effective open-world personalization and a favorable balance between user preference and image fidelity. FedPAIE thus connects decentralized preference learning with efficient personalized image transformation without requiring paired user retouches.
Abstract (translated)
URL
https://arxiv.org/abs/2607.27659
PDF
-
BlindPSNR: A No-Reference Fidelity Predictor for Low-Light Image Enhancement
2026-07-30 03:33:52Mingzhe Lyu, Jinqiang Cui, Hong ZhangAbstract
Low-light image enhancement (LLIE) methods involve tunable parameters that are typically fixed, often leading to performance degradation when applied across scenes. Manually selecting the best configuration, however, can be time-consuming and not always practical. Peak signal-to-noise ratio (PSNR) is the natural fidelity criterion for automating parameter selection, yet it requires a ground-truth reference that is typically unavailable. To our knowledge, no learning-based method addresses no-reference PSNR prediction for low-light image enhancement; the natural surrogate, no-reference image quality assessment (NR-IQA), targets perceptual quality rather than signal fidelity, and all seven baselines we test achieve 0% top-1 selection accuracy on our benchmark. With paired training data, the ground-truth PSNR is analytically computable, providing exact supervision without a separate teacher network. Building on this, we propose BlindPSNR, a lightweight no-reference network that fuses the enhanced image with the degraded low-light input via windowed cross-attention and estimates PSNR through heteroscedastic regression. While a scalar-regression baseline achieves top-1 accuracy of 54.4%, BlindPSNR raises this to 89.5% with regret dropping from 1.62 dB to 0.026 dB, and generalizes to unseen datasets (SRCC = 0.61-0.67).
Abstract (translated)
URL
https://arxiv.org/abs/2607.27628
PDF
-
Low-light Image Enhancement via Multi-scale Attention combined with Fourier Transform
2026-07-27 04:55:23Wenbin Du, Jian Long, Zhu CaoAbstract
Low-light image enhancement (LLIE) aims to improve image quality and clarity in diverse and demanding low-illumination environments. However, existing deep learning-based LLIE methods struggle to accurately capture real-world illumination and restore texture details, largely because their algorithmic strengths remain underutilized. To address these issues, we present a supervised frequency domain deep learning network for LLIE, named multi-scale attention combined with the Fourier transform (MSFT) which adopts a U-shaped, one-stage architecture that infuses guidance from low-light images into the network by channeling it through multi-scale attention. We further fuse the amplitude information from priori channels with that of the low-light image in MSFT's self-created module, and carry out multi-scale guidance along with the network. Subsequently, to better enhance the faint feature, such as fine content and textures, and to better fuse global context confidence in the decoding stage, we separately introduce a multi-shape synergistic attention and a lightweight network that effectively integrate information in high-dimensional space to embed into the superlative feature space channel containing rich texture information. Extensive experiments conducted on LOL, SID, SMID, and SDSD datasets demonstrate that MSFT significantly outperforms state-of-the-art competitors. For example, compared with Retinexformer, our method achieves a peak signal-to-noise ratio of up to 41.76 decibels on the SDSD-outdoor dataset with an increase of 11.92 decibels and a structural similarity index of 0.988 with a 13.80% improvement.
Abstract (translated)
URL
https://arxiv.org/abs/2607.24002
PDF
-
Color Fundus Photography Analysis: Co-evolution of Data, Preprocessing, and Modeling toward Multimodal AI
2026-07-27 03:46:11Yu Li, Wengan He, Wenhui Xu, Lihong Jiang, Fan Xiao, Zhuohang Huang, Yuanzhu Liang, Jiayi Liu, Yuxi Chen, Yongsheng LuoAbstract
Color Fundus Photography (CFP) is a primary non-invasive imaging modality for large-scale screening of ophthalmic and systemic diseases. Existing surveys mainly summarize task-specific algorithms, datasets, or preprocessing techniques independently, lacking a unified perspective on their co-evolution with modern artificial intelligence. This review provides an integrated overview of CFP AI through the interplay of dataset evolution, preprocessing paradigms, and modeling frameworks. We show that CFP datasets have evolved from small single-center collections with task-specific labels to large multi-center resources featuring multimodal pairings and longitudinal clinical records. Preprocessing has progressed from conventional image enhancement to neural data-engineering pipelines, hardware-aware token optimization, and self-supervised imputation for incomplete electronic health records (EHRs). Meanwhile, modeling has advanced from convolutional neural networks (CNNs) to vision foundation models, state space models (SSMs), and multimodal expert architectures. At the multimodal frontier, CFP is increasingly integrated with EHRs and longitudinal patient information, enabling more comprehensive clinical reasoning beyond isolated image analysis. We conclude that future progress depends on the collaborative optimization of datasets, preprocessing, and multimodal modeling, providing a roadmap toward robust clinical deployment, improved cross-domain generalization, and resource-efficient edge intelligence.
Abstract (translated)
URL
https://arxiv.org/abs/2607.23972
PDF
-
DY-LUT: Depth-Aware YCbCr Lookup Tables for Real-Time Underwater Image Enhancement
2026-07-24 14:50:28Cunhao Zhu, Xiangtao Kong, Dongliang Xu, Zhiheng Zhang, Tianyu Wang, Yue YaoAbstract
Underwater image enhancement is challenged by spatially non-uniform, wavelength-dependent attenuation. Propagation distance and wavelength govern this degradation, while YCbCr separates luminance from chrominance for restoration. We propose DY-LUT, a depth-aware YCbCr lookup-table framework for real-time enhancement. A dual-branch encoder predicts image-level fusion weights and a joint pair of pixel-wise degradation indices from image and depth features. These quantities condition learnable 4D LUTs, followed by lightweight local refinement. DY-LUT preserves traditional LUT efficiency while enabling depth-conditioned, spatially adaptive restoration. With externally supplied depth, its 3.56M-parameter enhancement network achieves competitive quality on UIEB-90 and LSUI and runs $9$--$304\times$ faster than representative high-capacity baselines. Adaptive inference further maintains real-time performance ($\sim7$ ms) for 4K UIQAD images. DY-LUT also benefits downstream detection and feature matching. Ablations show that YCbCr is a more effective basis than RGB for depth-conditioned lookup, while the jointly learned indices further improve adaptive querying. These results provide a physically grounded route to efficient UIE on practical platforms.
Abstract (translated)
URL
https://arxiv.org/abs/2607.22801
PDF
-
CLUIE: Clustering-Aware Recurrent Propagation with Local Structural Compensation for Underwater Image Enhancement
2026-07-23 16:09:36Kui Jiang, Zefan Feng, Laibin Chang, Yan Luo, Junjun Jiang, Xiaopeng FanAbstract
Underwater image enhancement remains challenging due to wavelength-dependent light absorption, scattering, and backscattering, which jointly cause color distortion, contrast degradation, and detail loss. Since these degradations vary with scene depth and imaging conditions, different regions within the same image often exhibit heterogeneous degradation patterns and thus require region-adaptive restoration. Although visual RWKV models offer an efficient linear-complexity solution for long-range dependency modeling, their predefined scanning orders are content-agnostic and therefore fail to adapt recurrent state propagation to spatially non-uniform restoration demands. To address this limitation, we propose a Clustering-aware RWKV framework, termed CRWKV, which reformulates the fixed recurrent propagation path of conventional RWKV into a content-adaptive token trajectory. Specifically, we introduce Clustering-aware Semantic Dynamic Reordering (CSDR), which groups tokens according to semantic feature similarity and derives a dynamic traversal order from inter-cluster contextual relations. This design enables WKV states to be accumulated along semantically correlated regions rather than fixed spatial or spectral orders. Since dynamic reordering may disrupt the local continuity of original spatial neighborhoods, we further propose Dark-response Modulated Local Propagation (DMLP), which extracts local structural responses via depth-wise convolution and adaptively modulates their propagation strength using a neighborhood-aware pseudo-dark response map. In this way, local structural cues are compensated before recurrent aggregation while preserving content-adaptive long-range modeling. Extensive experiments on multiple underwater image enhancement benchmarks demonstrate that CRWKV achieves state-of-the-art quantitative performance and superior visual quality.
Abstract (translated)
URL
https://arxiv.org/abs/2607.21467
PDF
-
Luminosity-Adaptive Contrast Enhancement Using CLAHE for Retinal Fundus Images with Quantitative Validation and Comparative Analysis
2026-07-20 08:39:13K. Mithra, Prem Kumar SanthanamAbstract
Background: Retinal fundus imaging is central to the early diagnosis of sight-threatening conditions including diabetic retinopathy, glaucoma, and retinal vein occlusion. Clinical utility of fundus images is routinely compromised by non-uniform illumination, motion blur, and low contrast - artefacts that increase the risk of diagnostic error. Effective image enhancement is therefore a prerequisite for reliable computer-aided ophthalmic diagnosis. Methods: This study proposes a two-stage image enhancement pipeline combining luminosity correction via HSV colour space decomposition with Contrast Limited Adaptive Histogram Equalization (CLAHE) applied exclusively to the Value (V) channel. Experiments are conducted on the publicly available DRIVE dataset (40 retinal fundus images, 584 x 565 pixels, Canon CR5 camera, ophthalmologist-annotated ground truth). Quantitative evaluation employs Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Contrast-to-Noise Ratio (CNR). Baseline comparisons include standard Histogram Equalization (HE) and Adaptive Histogram Equalization (AHE). A binary masking step is subsequently applied to isolate hyper-reflective regions consistent with vascular pathology. Results: The proposed method achieves PSNR = 29.3 dB, SSIM = 0.91, and CNR = 3.12 - outperforming HE (PSNR = 21.4 dB, SSIM = 0.74) and AHE (PSNR = 23.1 dB, SSIM = 0.79) across all metrics, with an average processing time of 0.14 seconds per image. Conclusions: The combined luminosity-CLAHE pipeline yields measurably superior contrast and structural fidelity compared to established baseline methods, with processing speed compatible with clinical screening workflows. Limitations and directions for deep-learning-based comparison are discussed.
Abstract (translated)
URL
https://arxiv.org/abs/2607.17691
PDF
-
Adaptive Contrast Enhancement and Optimised Feature Matching for RootSIFT-Based Palm-Vein Recognition
2026-07-17 16:04:15Kaveen Perera, Fouad Khelifi, Ammar BelatrecheAbstract
Palm-vein recognition is a highly secure biometric modality due to the uniqueness and subcutaneous nature of vein patterns. However, low contrast in palm-vein images, caused by NIR light scattering and sensor limitations, remains a significant challenge. To address this, we propose the Intensity-Limited Adaptive Contrast Stretching with Bidirectional Gaussian-weighted Overlapping Tiles (ILACS-BGOT) method, an enhancement of the previously developed ILACS with Layered Gaussian-weighted Overlapping Tiles (ILACS-LGOT) technique. ILACS enhances local contrast, while BGOT mitigates blocky artefacts. This study further integrates RootSIFT features with KNN+RT and incorporates the previously introduced Mean and Median Distance (MMD) filter to investigate the parameter variations of both MMD and RT, and their impact on recognition performance. A comprehensive analysis was conducted across three benchmark datasets (CASIA, PolyU, and PUT), using 42 combinations of MMD filter thresholds and RT values. Results were evaluated using EER and Accuracy. Findings reveal that higher template sizes improve performance, while varying MMD thresholds reflect dataset-specific rotational variations. The proposed system demonstrates superior generalisability, achieving significant improvements in both EER and Accuracy over existing methods. Furthermore, the underlying ILACS-BGOT mechanism suggests potential applicability beyond palm vein recognition to other biometric modalities such as finger vein and palmprint recognition, and more generally to low-contrast image enhancement across computer vision applications.
Abstract (translated)
URL
https://arxiv.org/abs/2607.16077
PDF
-
WREN: Low Light Image Enhancement Using Retinex theory-based Double U-Net-like Structures
2026-07-17 03:56:28Reina Kaneko, Junya Hara, Hiroshi Higashi, Yuichi TanakaAbstract
This paper proposes a neural network for low light image enhancement (LLIE) based on retinex theory to make LLIE robust for various dynamic range scenes. The retinex theory is an image formulation model inspired by a human color perception hypothesis, where a low light image is decomposed into intrinsic color context (i.e., reflectance map) and scene-dependent illumination (i.e., illumination map). Due to non-uniqueness of its decomposition, existing retinex-based LLIE methods often fail to achieve stable decomposition, which lead to over-enhancement. Typically, they are sensitive to the dynamic ranges that vary in different lighting conditions. To tackle this issue, we propose WREN: An LLIE neural network with double U-Net-like structures. WREN consists of two U-Net-like sub-networks. The first network has one encoder and two decoders that decompose an input image into the reflectance and illumination maps. The second network with a customized Transformer block between an encoder and a decoder only enhances the illumination map obtained from the first network: This completely follows the assumption of the retinex theory. Finally, the enhanced illumination map is recombined with the reflectance map. The network is trained end-to-end with a scale-invariant loss function, which gives robustness against the illumination scaling. Numerical results show that our method achieves the state-of-the-art performance across multiple datasets. Our code is available online.
Abstract (translated)
URL
https://arxiv.org/abs/2607.15604
PDF
-
Thresholded Cross-Attention for Reliable Intensity-Chromaticity Fusion in Low-Light Image Enhancement
2026-07-15 15:06:16Yanyi Wu, Xu Zhang, Junkai Chen, Laibin Chang, Jiaqi Ma, Shi Chen, Linwei Zhu, Jianglei Di, Huan ZhangAbstract
Low-Light Image Enhancement (LLIE) requires a careful balance among noise suppression, color fidelity, and efficiency. Recent HVI-based methods alleviate color entanglement by decoupling intensity and chromaticity, yet how reliably the two streams are fused again is an overlooked factor that largely determines the final quality. We observe that the confidence of cross-stream attention is strongly layer-dependent, so the fixed-quota selection of Top-K sparse attention is mismatched to it, discarding informative dependencies in some layers while retaining noisy ones in others. Motivated by this observation, we propose TCA-Net, a network built around Thresholded Cross-Attention that targets reliable intensity-chromaticity fusion in the HVI space rather than introducing yet another color representation. At its core, TCA replaces the rigid Top-K quota with a fixed confidence threshold whose retained cardinality is input- and layer-adaptive, retaining only high-confidence cross-stream interactions while suppressing unreliable ones. Around this core, two complementary designs clean up the fusion before and after it: a Phase-guided Fourier Interaction Module provides a structure-aware brightness initialization for the intensity stream prior to fusion, and a Decoupled Dual-Stream Guidance Module constructs residual intensity features to suppress chromaticity leakage during reconstruction. A Scale-Aware Consistency Regularization further improves structural robustness under scale perturbations during training. Extensive experiments on LOL-v1, LOL-v2, Sony-Total-Dark, and LSRW-Huawei demonstrate that TCA-Net delivers competitive restoration accuracy, improved color fidelity, and a compact parameter size.
Abstract (translated)
URL
https://arxiv.org/abs/2607.13925
PDF
-
Virtual Chromoendscopy with Tunable Visibility Enhancement
2026-07-14 06:45:33Yuhi Kanno, Yusuke Monno, Sho Suzuki, Tomohiro Tada, Masatoshi OkutomiAbstract
Chromoendoscopy (CE) is a common clinical practice that sprays indigo carmine blue dye onto the gastric surface to improve the visibility of gastric lesions, such as an early cancer. While CE is effective in detecting the lesions, preparing and spraying the dye needs additional cost and time, which is undesirable both for patients and medical practitioners. To overcome this issue, virtual chromoendoscopy (V-CE) was recently proposed, which applies a learned image translation model to virtually generate a CE image from a standard endoscopy (SE) image. In this paper, we propose virtual enhanced chromoendoscopy (V-ECE) that combines V-CE with image enhancement techniques to further improve the visibility of gastric lesions. Because a desired enhancement level depends on the inspected lesion and the practitioner's preference, we introduce a novel image translation model that can generate V-ECE images using an enhancement level tunable by a user. Experimental results demonstrate that our proposed model can plausibly generate V-ECE images with various enhancement levels using a unified model.
Abstract (translated)
URL
https://arxiv.org/abs/2607.12416
PDF
-
Leveraging Color Naming for Image Enhancement
2026-07-09 07:35:55David Serrano-Lozano, Luis Herranz, Michael S. Brown, Javier Vazquez-CorralAbstract
Enhancing images to make them visually appealing is a persistent challenge in computer vision. Many deep-learning methods train models on paired datasets to replicate expert editing styles. However, these approaches struggle with two key issues: (1) interpretability and (2) a parametrization suitable for user adjustments. To address these challenges, we present NamedCurves+, an approach inspired by the concept of Color Naming, a universal set of familiar colors widely used in software tools for intuitive editing. Our method integrates color names into a learning-based framework, enabling global adjustments for each named color through tone curves. To address local image variations, we incorporate a transformer block that captures spatial dependencies, enabling context-aware edits across the image. NamedCurves+ enhances the retouching process's interpretability and supports user interaction, allowing flexible modifications of individual tone curves to refine the retouched image according to personal preferences. Extensive experiments on tasks such as image retouching, tone mapping, and exposure correction demonstrate that NamedCurves+ outperforms state-of-the-art methods. Notably, our approach is both explainable, as the tone curves explicitly represent how each color name contributes to the enhancement, and interactive, allowing users to customize the retouching process and achieve results tailored to their liking.
Abstract (translated)
URL
https://arxiv.org/abs/2607.08185
PDF
-
Geometry-aware Depth-guided Representation Learning for Structure-preserving Low-light Image Enhancement
2026-07-06 12:46:15Fang Gao, Jiongkai Qin, Jiabao Wang, Jingfeng Tang, Ming Cheng, Hanbo Zheng, Qingbao Huang, Cheng WuAbstract
Low-light degradation reduces image visibility and weakens structural cues that are important for visual representation and scene understanding. Existing low-light image enhancement methods mainly focus on appearance restoration, while insufficiently exploiting scene geometry to preserve structural consistency. To address this limitation, this paper proposes a Depth-guided Multi-scale Attention Network (DMSA-Net) for geometry-aware low-light image enhancement. DMSA-Net introduces depth-related structural priors into low-light representation learning through reflectance-geometry interaction. A Retinex-based decomposition module is first used to obtain illumination-invariant reflectance representations, from which depth cues are inferred to characterize scene structure under degraded illumination. A multi-scale depth-guided fusion strategy is then embedded into a hierarchical encoder-decoder architecture, where depth-aware attention adaptively integrates geometric and appearance features. Experiments on several benchmark datasets show that DMSA-Net achieves effective low-light restoration while improving structural preservation. Moreover, we construct LOL-D, a depth-augmented low-light dataset, to facilitate research on geometry-aware low-light vision.
Abstract (translated)
URL
https://arxiv.org/abs/2607.05005
PDF
-
MambaLIE: Scene Light Intensity-Boosted Low-Light Image Enhancement with State Space Model
2026-07-03 06:46:45Wanshu Fan, Xiangyu Li, Cong Wang, Kin-man Lam, Xin Yang, Haiyan Zhang, Dongsheng ZhouAbstract
Images captured by consumer electronic devices, such as mobile phones and digital cameras, often suffer from low-light degradation due to sensor limitations and imaging pipelines, which degrades visual quality and affects downstream vision tasks. Existing methods based on Convolutional Neural Networks (CNNs) and Transformers have dominated current low-light image enhancement (LIE) due to their excellent ability to model hierarchical features. However, CNNs operate in local receptive fields that cannot model long-range dependencies, while Transformers overcome this problem but incur substantial computational costs. To address these challenges, we propose MambaLIE, a Scene Light Intensity-Boosted Low-Light Image Enhancement method based on a State Space Model (SSM). We first introduce scene light intensity to improve the structural distribution of illumination, which is then gated with the low-light input to guide enhancement. To better model the illumination while maintaining computational efficiency, we propose the Locally Enhanced State Space Model (LESSM) for efficient light enhancement. Our LESSM contains two branches: an SSM branch and a Local Enhanced branch, where the former is used to model the long-range dependencies with linear time complexity, while the latter is used to enhance local feature representations. Extensive experiments demonstrate that MambaLIE outperforms state-of-the-art CNN-based and Transformer-based LIE methods on four widely used synthetic benchmarks and five publicly available real-world benchmarks in terms of accuracy, speed, and model size, making it suitable for practical deployment on resource-constrained devices.
Abstract (translated)
URL
https://arxiv.org/abs/2607.03013
PDF
-
Aircraft Detection in Satellite Imagery using Deep Learning Object Detectors
2026-07-02 18:43:38Mujahir Hussain Abbasi (Department of Information Technology Maulana Abul Kalam Azad University of Technology, Kolkata, West Bengal, India), S. Beghin (Bose School of Computational,Physical Sciences Kristu Jayanti, Bengaluru, Karnataka), A. T. R. Krishna Priya (AB Technologies Nagercoil, Kanyakumari, Tamil Nadu, India), P. Jamuna (Department of Electrical,Electronics Engineering Nandha Engineering College, Erode, India), G. Anandhakumari (Department of Physics Erode Sengunthar Engineering College, Perundurai, India), S. Jaanaa Rubavathy (Department of Electrical,Electronics Engineering Saveetha School of Engineering, Saveetha University, Chennai, India)Abstract
The object detection in satellite imagery has garnered considerable attention due to its extensive real-world applications and the inherent challenges it presents, including noise, fluctuating image quality, and intricate backgrounds. This paper proposed a framework for object detection that combines image enhancement and Deep Learning (DL) to make detection more accurate. First, a Gabor filter is used to process the input image to bring out important features and reduce noise. Then, normalization is applied to make sure that the data is evenly distributed so that the model works properly. After that, a model based on YOLOv11 is used to quickly learn and find object features. The proposed method achieves a mAP of 95%, precision of 97%, recall of 85%, and F1-score of 91%, which demonstrates the superior aircraft detection performance. These results show the framework accurately identify aircraft in satellite imagery and is suitable for real-time applications such as surveillance, air traffic monitoring and remote sensing analysis.
Abstract (translated)
URL
https://arxiv.org/abs/2607.02699
PDF
-
Quantum-Inspired Vision: Leveraging Wave-Particle Duality for Low-Illumination Enhancement
Abstract
This study provides a theoretical expansion of the recent Data Relativistic Uncertainty (DRU) framework by formalizing a physics-to-AI paradigm for image enhancement. By modeling images as probabilistic wave functions rather than deterministic states, the paradigm explicitly integrates wave-particle duality to illustrate the system flow of how DRU leverages the intrinsic physical uncertainty of light, a dimension requiring further theoretical discussion. Consequently, this paradigm provides a rigorous Explainable AI (XAI) approach that enhances the interpretability of how DRU mitigates illumination bias and maintains robustness against data noise.
Abstract (translated)
URL
https://arxiv.org/abs/2607.01731
PDF
-
Joint Medical Image Enhancement and Segmentation with Diffusion-based Symbiotic Information Interaction
2026-06-30 07:50:44Ying Chen, Jinyue Li, Qiankun LiAbstract
Image quality is critical for accurate medical diagnosis. However, MRI, CT, and ultrasound images are often of low resolution and quality due to cost constraints, complicating the visualization of key anatomical structures and lesions. While such limitations are common in practice, traditional methods treat image enhancement as a separate preprocessing step, failing to fully leverage its potential synergy with image segmentation. To address this, we propose DiSIINet (Diffusion-based Symbiotic Information Interaction Network), which is built on the principle that enhancement and segmentation should mutually reinforce each other in a unified model. Based on Denoising Diffusion Implicit Models (DDIM), DiSIINet integrates an enhancement branch and a segmentation branch. These branches interact through a novel Symbiotic Information Interaction (SII) module, which facilitates dynamic, feature-level information exchange via cross-attention during the reverse diffusion process. This design enables both tasks to iteratively improve each other. The DDIM backbone ensures high-quality output and efficient inference through deterministic sampling. Experiments on multi-modal medical datasets (MRI, CT, ultrasound) show that DiSIINet achieves significant performance improvements compared to sequential or independent enhancement and segmentation approaches. The code is available at: this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2607.00058
PDF
-
Real-Time Underwater Image Enhancement via Frequency-Guided Dual-Path Attention
2026-06-29 13:59:02Leshen Zhang, Ao Li, Ce ZhuAbstract
Real-time underwater image enhancement (UIE) is crucial for mobile underwater photography and autonomous robotic systems, where practical deployment typically requires low latency and compact models under constrained computational resources. Recent ultra-lightweight CNNs based on structural re-parameterization meet these constraints but operate purely in the spatial domain, ignoring the frequency-sensitive nature of underwater degradation. To address this, we propose a lightweight UIE framework that integrates two key components: a Multi-Branch Reparameterizable Convolution with Fixed DCT Priors (MBRConv-DCT) that injects structured directional frequency priors during training, and a Frequency-Guided Dual-Path Attention (FGDPA) module that fuses spatial and spectral cues via a dual-path design for adaptive feature modulation. Both components are fully compatible with structural re-parameterization: the convolution branch introduces zero additional inference cost after re-parameterization, while the attention module incurs only a minimal computational overhead. Experiments show our model achieves state-of-the-art performance with only 4.23K parameters and 600+ FPS, outperforming much larger methods in both quantitative metrics and visual quality. Code is available at this https URL.
Abstract (translated)
URL
https://arxiv.org/abs/2606.30314
PDF
