Image Restoration
473 papers with code • 1 benchmarks • 12 datasets
Image Restoration is a family of inverse problems for obtaining a high quality image from a corrupted input image. Corruption may occur due to the image-capture process (e.g., noise, lens blur), post-processing (e.g., JPEG compression), or photography in non-ideal conditions (e.g., haze, motion blur).
Libraries
Use these libraries to find Image Restoration models and implementationsDatasets
SIDD
CBSD68
Raindrop
PIRM
TinyPerson
Real Rain Dataset
UHDM
Fingerprint inpainting and denoising
Subtasks
Latest papers with no code
Multispectral Image Restoration by Generalized Opponent Transformation Total Variation
Here opponent transformations for multispectral images are generalized from a well-known opponent transformation for color images.
CasSR: Activating Image Power for Real-World Image Super-Resolution
In particular, we develop a cascaded controllable diffusion model that aims to optimize the extraction of information from low-resolution images.
Divide-and-Conquer Posterior Sampling for Denoising Diffusion Priors
In this work, we take a different approach and utilize the specific structure of the DDM prior to define a set of intermediate and simpler posterior sampling problems, resulting in a lower approximation error compared to previous methods.
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.
How Powerful Potential of Attention on Image Restoration?
Our designs provide a closer look at the attention mechanism and reveal that some simple operations can significantly affect the model performance.
Solving General Noisy Inverse Problem via Posterior Sampling: A Policy Gradient Viewpoint
Solving image inverse problems (e. g., super-resolution and inpainting) requires generating a high fidelity image that matches the given input (the low-resolution image or the masked image).
D-YOLO a robust framework for object detection in adverse weather conditions
Adverse weather conditions including haze, snow and rain lead to decline in image qualities, which often causes a decline in performance for deep-learning based detection networks.
Boosting Image Restoration via Priors from Pre-trained Models
Pre-trained models with large-scale training data, such as CLIP and Stable Diffusion, have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions.
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.
Implicit Image-to-Image Schrodinger Bridge for CT Super-Resolution and Denoising
As a promising alternative, the Image-to-Image Schr\"odinger Bridge (I2SB) initializes the generative process from corrupted images and integrates training techniques from conditional diffusion models.
