Paper Reading AI Learner

Perceiving Unknown in Dark from Perspective of Cell Vibration

2020-06-03 13:39:10
Xiaozhou Lei, Minrui Fei, Wenju Zhou, Huiyu Zhou

Abstract

Low light very likely leads to the degradation of image quality and even causes visual tasks' failure. Existing image enhancement technologies are prone to over-enhancement or color distortion, and their adaptability is fairly limited. In order to deal with these problems, we utilise the mechanism of biological cell vibration to interpret the formation of color images. In particular, we here propose a simple yet effective cell vibration energy (CVE) mapping method for image enhancement. Based on a hypothetical color-formation mechanism, our proposed method first uses cell vibration and photoreceptor correction to determine the photon flow energy for each color channel, and then reconstructs the color image with the maximum energy constraint of the visual system. Photoreceptor cells can adaptively adjust the feedback from the light intensity of the perceived environment. Based on this understanding, we here propose a new Gamma auto-adjustment method to modify Gamma values according to individual images. Finally, a fusion method, combining CVE and Gamma auto-adjustment (CVE-G), is proposed to reconstruct the color image under the constraint of lightness. Experimental results show that the proposed algorithm is superior to six state of the art methods in avoiding over-enhancement and color distortion, restoring the textures of dark areas and reproducing natural colors. The source code will be released at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2006.02271

PDF

https://arxiv.org/pdf/2006.02271.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot