Beyond a gaussian denoiser: Residual learning of deep cnn for image denoising
The discriminative model learning for image denoising has been recently attracting
considerable attentions due to its favorable denoising performance. In this paper, we take …
considerable attentions due to its favorable denoising performance. In this paper, we take …
A brief review of image denoising algorithms and beyond
The recent advances in hardware and imaging systems made the digital cameras
ubiquitous. Although the development of hardware has steadily improved the quality of …
ubiquitous. Although the development of hardware has steadily improved the quality of …
Image restoration via simultaneous nonlocal self-similarity priors
Through exploiting the image nonlocal self-similarity (NSS) prior by clustering similar
patches to construct patch groups, recent studies have revealed that structural sparse …
patches to construct patch groups, recent studies have revealed that structural sparse …
Practical blind image denoising via Swin-Conv-UNet and data synthesis
While recent years have witnessed a dramatic upsurge of exploiting deep neural networks
toward solving image denoising, existing methods mostly rely on simple noise assumptions …
toward solving image denoising, existing methods mostly rely on simple noise assumptions …
Recurrent inference machines for solving inverse problems
Much of the recent research on solving iterative inference problems focuses on moving
away from hand-chosen inference algorithms and towards learned inference. In the latter …
away from hand-chosen inference algorithms and towards learned inference. In the latter …
Image denoising method based on a deep convolution neural network
F Zhang, N Cai, J Wu, G Cen, H Wang… - IET Image …, 2018 - Wiley Online Library
Image denoising is still a challenging problem in image processing. The authors propose a
novel image denoising method based on a deep convolution neural network (DCNN) …
novel image denoising method based on a deep convolution neural network (DCNN) …
A hybrid structural sparsification error model for image restoration
Recent works on structural sparse representation (SSR), which exploit image nonlocal self-
similarity (NSS) prior by grouping similar patches for processing, have demonstrated …
similarity (NSS) prior by grouping similar patches for processing, have demonstrated …
Adaptive image denoising by targeted databases
We propose a data-dependent denoising procedure to restore noisy images. Different from
existing denoising algorithms which search for patches from either the noisy image or a …
existing denoising algorithms which search for patches from either the noisy image or a …
Self-tuned deep super resolution
Deep learning has been successfully applied to image super resolution (SR). In this paper,
we propose a deep joint super resolution (DJSR) model to exploit both external and self …
we propose a deep joint super resolution (DJSR) model to exploit both external and self …
Learning super-resolution jointly from external and internal examples
Single image super-resolution (SR) aims to estimate a high-resolution (HR) image from a
low-resolution (LR) input. Image priors are commonly learned to regularize the, otherwise …
low-resolution (LR) input. Image priors are commonly learned to regularize the, otherwise …