Denoising
1893 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
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Libraries
Use these libraries to find Denoising models and implementations
SIDD
SID
CBSD68
PolyU
DND
CRVD
iris
FMD
Subtasks
Latest papers with no code
G-HOP: Generative Hand-Object Prior for Interaction Reconstruction and Grasp Synthesis
We propose G-HOP, a denoising diffusion based generative prior for hand-object interactions that allows modeling both the 3D object and a human hand, conditioned on the object category.
FreeDiff: Progressive Frequency Truncation for Image Editing with Diffusion Models
Precise image editing with text-to-image models has attracted increasing interest due to their remarkable generative capabilities and user-friendly nature.
Optical Image-to-Image Translation Using Denoising Diffusion Models: Heterogeneous Change Detection as a Use Case
We introduce an innovative deep learning-based method that uses a denoising diffusion-based model to translate low-resolution images to high-resolution ones from different optical sensors while preserving the contents and avoiding undesired artifacts.
Factorized Diffusion: Perceptual Illusions by Noise Decomposition
And we explore a decomposition by a motion blur kernel, which produces images that change appearance under motion blurring.
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening
Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images.
Leveraging Fine-Grained Information and Noise Decoupling for Remote Sensing Change Detection
Next, a shape-aware and a brightness-aware module are designed to improve the capacity for representation learning.
Unsupervised Microscopy Video Denoising
In this paper, we introduce a novel unsupervised network to denoise microscopy videos featured by image sequences captured by a fixed location microscopy camera.
OneActor: Consistent Character Generation via Cluster-Conditioned Guidance
Comprehensive experiments show that our method outperforms a variety of baselines with satisfactory character consistency, superior prompt conformity as well as high image quality.
Generating Human Interaction Motions in Scenes with Text Control
Our approach begins with pre-training a scene-agnostic text-to-motion diffusion model, emphasizing goal-reaching constraints on large-scale motion-capture datasets.
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.
