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We demonstrate the capabilities of the proposed 3-stage structure with three typical color image restoration tasks: color image demosaicking, color compression artifacts reduction, and real-world color image denoising.
Blind and universal image denoising consists of using a unique model that denoises images with any level of noise.
Ranked #1 on Grayscale Image Denoising on BSD68 sigma20
Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling.
Ranked #1 on Color Image Denoising on BSD68 sigma25
Filtering real-world color images is challenging due to the complexity of noise that can not be formulated as a certain distribution.
In this work, we propose a new method for natural image denoising that trains a deep neural network to determine whether patches in a noisy image input share common underlying patterns.
In this paper, we propose a novel residual dense network (RDN) to address this problem in image SR. We fully exploit the hierarchical features from all the convolutional layers.
Ranked #1 on Color Image Denoising on CBSD68 sigma50
We apply MemNet to three image restoration tasks, i. e., image denosing, super-resolution and JPEG deblocking.
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).
Ranked #1 on Color Image Denoising on BSD68 sigma5