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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Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
#2 best model for Image Denoising on BSD68 sigma10
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.
SOTA for 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).
#4 best model for Image Denoising on BSD68 sigma15
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.
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.
#2 best model for Image Denoising on Urban100 sigma70
The main contributions of this work are: (1) Unlike existing methods that measure self-similarity in an isolated manner, the proposed non-local module can be flexibly integrated into existing deep networks for end-to-end training to capture deep feature correlation between each location and its neighborhood.
SOTA for Image Denoising on BSD68 sigma15
In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods.