Image Denoising
420 papers with code • 19 benchmarks • 17 datasets
Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.
( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )
Benchmarks
These leaderboards are used to track progress in Image Denoising
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Libraries
Use these libraries to find Image Denoising models and implementations
FFHQ
BSD
Urban100
SIDD
SID
CBSD68
PolyU
DND
ELD
CRVD
Latest papers with no code
Transfer CLIP for Generalizable Image Denoising
Image denoising is a fundamental task in computer vision.
QSMDiff: Unsupervised 3D Diffusion Models for Quantitative Susceptibility Mapping
In this work, we developed a 3D image patch-based diffusion model, namely QSMDiff, for robust QSM reconstruction across different scan parameters, alongside simultaneous super-resolution and image-denoising tasks.
Improving Robustness to Model Inversion Attacks via Sparse Coding Architectures
Recent model inversion attack algorithms permit adversaries to reconstruct a neural network's private training data just by repeatedly querying the network and inspecting its outputs.
Low-Trace Adaptation of Zero-shot Self-supervised Blind Image Denoising
Deep learning-based denoiser has been the focus of recent development on image denoising.
WIA-LD2ND: Wavelet-based Image Alignment for Self-supervised Low-Dose CT Denoising
Second, to better capture high-frequency components and detailed information, Frequency-Aware Multi-scale Loss (FAM) is proposed by effectively utilizing multi-scale feature space.
A Spectrum-based Image Denoising Method with Edge Feature Enhancement
Image denoising stands as a critical challenge in image processing and computer vision, aiming to restore the original image from noise-affected versions caused by various intrinsic and extrinsic factors.
Decoupled Data Consistency with Diffusion Purification for Image Restoration
To solve image restoration problems, many existing techniques achieve data consistency by incorporating additional likelihood gradient steps into the reverse sampling process of diffusion models.
Fast, nonlocal and neural: a lightweight high quality solution to image denoising
In this letter, we propose a solution to both issues by combining a nonlocal algorithm with a lightweight residual CNN.
On normalization-equivariance properties of supervised and unsupervised denoising methods: a survey
Image denoising is probably the oldest and still one of the most active research topic in image processing.
A unified framework of non-local parametric methods for image denoising
We propose a unified view of non-local methods for single-image denoising, for which BM3D is the most popular representative, that operate by gathering noisy patches together according to their similarities in order to process them collaboratively.
