Grayscale Image Denoising
9 papers with code • 41 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Grayscale Image Denoising
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BSD
Urban100
Set12
Latest papers
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning
Nonlocal self-similarity within natural images has become an increasingly popular prior in deep-learning models.
KBNet: Kernel Basis Network for Image Restoration
zhangyi-3/kbnet
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In this paper, we propose a kernel basis attention (KBA) module, which introduces learnable kernel bases to model representative image patterns for spatial information aggregation.
Adversarial Distortion Learning for Medical Image Denoising
mogvision/adl
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The proposed ADL consists of two auto-encoders: a denoiser and a discriminator.
Improving Image Restoration by Revisiting Global Information Aggregation
Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
SwinIR: Image Restoration Using Swin Transformer
In particular, the deep feature extraction module is composed of several residual Swin Transformer blocks (RSTB), each of which has several Swin Transformer layers together with a residual connection.
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning
In addition, we leverage the model's interpretable construction to propose an augmentation of the network's thresholds that enables state-of-the-art blind denoising performance and near-perfect generalization on noise-levels unseen during training.
LIDIA: Lightweight Learned Image Denoising with Instance Adaptation
This work proposes a novel lightweight learnable architecture for image denoising, and presents a combination of supervised and unsupervised training of it, the first aiming for a universal denoiser and the second for adapting it to the incoming image.
Index Network
poppinace/indexnet_matting
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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.
