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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
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
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
We apply MemNet to three image restoration tasks, i. e., image denosing, super-resolution and JPEG deblocking.
To address this issue, here we propose a novel feature space deep residual learning algorithm that outperforms the existing residual learning.
Ranked #3 on Color Image Denoising on CBSD68 sigma50
Convex relaxations of nonconvex multilabel problems have been demonstrated to produce superior (provably optimal or near-optimal) solutions to a variety of classical computer vision problems.
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.