Denoising
1927 papers with code • 5 benchmarks • 20 datasets
Denoising is a task in image processing and computer vision that aims to remove or reduce noise from an image. Noise can be introduced into an image due to various reasons, such as camera sensor limitations, lighting conditions, and compression artifacts. The goal of denoising is to recover the original image, which is considered to be noise-free, from a noisy observation.
( Image credit: Beyond a Gaussian Denoiser )
Benchmarks
These leaderboards are used to track progress in Denoising
| Trend | Dataset | Best Model | Paper | Code | Compare |
|---|---|---|---|---|---|
|
|||||
|
|||||
|
|||||
|
|||||
|
Libraries
Use these libraries to find Denoising models and implementations
SIDD
SID
CBSD68
PolyU
DND
CRVD
iris
FMD
Subtasks
Latest papers
Assessing The Impact of CNN Auto Encoder-Based Image Denoising on Image Classification Tasks
The goal is to enhance the accuracy and robustness of defect detection by integrating noise detection and denoising into the classification pipeline.
WiTUnet: A U-Shaped Architecture Integrating CNN and Transformer for Improved Feature Alignment and Local Information Fusion
woldier/witunet
•
•
Low-dose computed tomography (LDCT) has become the technology of choice for diagnostic medical imaging, given its lower radiation dose compared to standard CT, despite increasing image noise and potentially affecting diagnostic accuracy.
RoofDiffusion: Constructing Roofs from Severely Corrupted Point Data via Diffusion
Accurate completion and denoising of roof height maps are crucial to reconstructing high-quality 3D buildings.
NIR-Assisted Image Denoising: A Selective Fusion Approach and A Real-World Benchmark Dataset
ronjonxu/naid
•
•
Despite the significant progress in image denoising, it is still challenging to restore fine-scale details while removing noise, especially in extremely low-light environments.
TBSN: Transformer-Based Blind-Spot Network for Self-Supervised Image Denoising
For channel self-attention, we observe that it may leak the blind-spot information when the channel number is greater than spatial size in the deep layers of multi-scale architectures.
Taming Stable Diffusion for Text to 360° Panorama Image Generation
Generative models, e. g., Stable Diffusion, have enabled the creation of photorealistic images from text prompts.
Classifier-guided neural blind deconvolution: a physics-informed denoising module for bearing fault diagnosis under heavy noise
asdvfghg/classbd
•
•
Blind deconvolution (BD) has been demonstrated as an efficacious approach for extracting bearing fault-specific features from vibration signals under strong background noise.
ConsistencyDet: A Robust Object Detector with a Denoising Paradigm of Consistency Model
In the present study, we introduce a novel framework designed to articulate object detection as a denoising diffusion process, which operates on the perturbed bounding boxes of annotated entities.
Single Stage Adaptive Multi-Attention Network for Image Restoration
anas-zafar/SSAMAN
•
•
IEEE Transactions on Image Processing 2024
In this paper, we propose a novel and computationally efficient architecture Single Stage Adaptive Multi-Attention Network (SSAMAN) for image restoration tasks, particularly for image denoising and image deblurring.
scRDiT: Generating single-cell RNA-seq data by diffusion transformers and accelerating sampling
The method is a neural network constructed based on Denoising Diffusion Probabilistic Models (DDPMs) and Diffusion Transformers (DiTs).
