Grayscale Image Denoising
41개 벤치마크 · 논문 13편 · 이 태스크의 논문 보기 →
Benchmarks
BSD68 sigma15
BSD68 sigma25
BSD68 sigma50
Urban100 sigma25
Urban100 sigma50
Set12 sigma15
Set12 sigma50
Urban100 sigma15
Set12 sigma25
BSD200 sigma30
BSD200 sigma50
BSD200 sigma70
BSD68 sigma70
urban100 sigma15
BSD200 sigma10
BSD68 sigma10
BSD68 sigma30
BSD68 sigma35
BSD68 sigma75
Set12 sigma30
Urban100 sigma70
BSD68 sigma20
BSD68 sigma40
BSD68 sigma45
BSD68 sigma5
BSD68 sigma55
BSD68 sigma60
BSD68 sigma65
Clip300 sigma15
Clip300 sigma25
Clip300 sigma35
Clip300 sigma50
Clip300 sigma60
Hanzi
Kodak24 sigma10
Kodak24 sigma30
Kodak24 sigma50
Kodak24 sigma70
Set12 sigma70
Urban100 sigma10
Urban100 sigma30
Most implemented
Restormer: Efficient Transformer for High-Resolution Image Restoration
SwinIR: Image Restoration Using Swin Transformer
Index Network
Improving Image Restoration by Revisiting Global Information Aggregation
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning
KBNet: Kernel Basis Network for Image Restoration
Papers
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration
While vision transformers achieve significant breakthroughs in various image restoration (IR) tasks, it is still challenging to efficiently scale them across multiple types of degradations and resolutions. In this paper,…
DeblurringDemosaickingDenoisingGrayscale Image Denoising+4Hierarchical Information Flow for Generalized Efficient Image Restoration
While vision transformers show promise in numerous image restoration (IR) tasks, the challenge remains in efficiently generalizing and scaling up a model for multiple IR tasks. To strike a balance between efficiency and …
Color Image DenoisingGrayscale Image DenoisingImage DeblurringImage Restoration+4Fast 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. Despite their successful image restoration performance, such models remain largely uninterpretable due to t…
DenoisingDictionary LearningGrayscale Image DenoisingImage Denoising+1SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions
Self-supervised image denoising implies restoring the signal from a noisy image without access to the ground truth. State-of-the-art solutions for this task rely on predicting masked pixels with a fully-convolutional neu…
Color Image DenoisingDenoisingGrayscale Image DenoisingImage Denoising+1KBNet: Kernel Basis Network for Image Restoration
How to aggregate spatial information plays an essential role in learning-based image restoration. Most existing CNN-based networks adopt static convolutional kernels to encode spatial information, which cannot aggregate …
Color Image DenoisingDeblurringDenoisingGrayscale Image Denoising+4Adversarial Distortion Learning for Medical Image Denoising
We present a novel adversarial distortion learning (ADL) for denoising two- and three-dimensional (2D/3D) biomedical image data. The proposed ADL consists of two auto-encoders: a denoiser and a discriminator. The denoise…
Color Image DenoisingDenoisingGrayscale Image DenoisingImage Denoising+1