Image Denoising is the task of removing noise from an image, e.g. the application of Gaussian noise to an image.
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In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.
Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
#3 best model for Image Compression Artifact Reduction on LIVE1 Quality 30
In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.
#2 best model for Grayscale Image Denoising on BSD200 sigma10
Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).
We fully exploit the hierarchical features from all the convolutional layers.
While deep convolutional neural networks (CNNs) have achieved impressive success in image denoising with additive white Gaussian noise (AWGN), their performance remains limited on real-world noisy photographs.
#3 best model for Denoising on Darmstadt Noise Dataset
Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising.
To exploit our relaxation, we propose the neural nearest neighbors block (N3 block), a novel non-local processing layer that leverages the principle of self-similarity and can be used as building block in modern neural network architectures.
By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.
SOTA for Scene Segmentation on SUN-RGBD