Paper Reading AI Learner

Identifying Recurring Patterns with Deep Neural Networks for Natural Image Denoising

2018-12-23 22:59:27
Zhihao Xia, Ayan Chakrabarti

Abstract

Image denoising methods must effectively model, implicitly or explicitly, the vast diversity of patterns and textures that occur in natural images. This is challenging, even for modern methods that leverage deep neural networks trained to regress to clean images from noisy inputs. Meanwhile, a number of traditional image restoration methods have demonstrated the benefits of relying on "internal" image statistics: using the fact that the variability of patterns within a single image is far more limited than that across various images and scenes. A key obstacle with such approaches, however, is in accurately identifying recurring patterns from within a noisy observation. In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether noisy patches in a given image input share common underlying patterns. Specifically, given a pair of noisy patches, this network predicts whether different transform sub-band coefficients of the original noise-free patches are similar. The denoising algorithm then aggregates matched coefficients to obtain an initial estimate of the clean image. We show that this yields higher quality results than previous internal statistics-based approaches. Moreover, by providing this estimate, along with the original noisy image, as input to a standard regression-based denoising network, we demonstrate that our method is able to achieve state-of-the-art denoising performance.

Abstract (translated)

URL

https://arxiv.org/abs/1806.05229

PDF

https://arxiv.org/pdf/1806.05229


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Attention Autonomous Bert Boundary_Detection Caption Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Drone Dynamic_Memory_Network Edge_Detection Embedding 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