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
BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution
BlindDiff seamlessly integrates the MAP-based optimization into DMs, which constructs a joint distribution of the low-resolution (LR) observation, high-resolution (HR) data, and degradation kernels for the data and kernel priors, and solves the blind SR problem by unfolding MAP approach along with the reverse process.
Ambient Diffusion Posterior Sampling: Solving Inverse Problems with Diffusion Models trained on Corrupted Data
utcsilab/ambient-diffusion-mri
•
•
We open-source our code and the trained Ambient Diffusion MRI models: https://github. com/utcsilab/ambient-diffusion-mri .
Efficient Diffusion Model for Image Restoration by Residual Shifting
zsyoaoa/resshift
•
•
While diffusion-based image restoration (IR) methods have achieved remarkable success, they are still limited by the low inference speed attributed to the necessity of executing hundreds or even thousands of sampling steps.
Continual All-in-One Adverse Weather Removal with Knowledge Replay on a Unified Network Structure
It considers the characteristics of the image restoration task with multiple degenerations in continual learning, and the knowledge for different degenerations can be shared and accumulated in the unified network structure.
Generalizing to Out-of-Sample Degradations via Model Reprogramming
ddghjikle/out-of-sample-restoration
•
•
To address this issue, we propose a model reprogramming framework, which translates out-of-sample degradations by quantum mechanic and wave functions.
Decoupling Degradations with Recurrent Network for Video Restoration in Under-Display Camera
The pixel array of light-emitting diodes used for display diffracts and attenuates incident light, causing various degradations as the light intensity changes.
FriendNet: Detection-Friendly Dehazing Network
In this paper, we raise an intriguing question: can the combination of image restoration and object detection enhance detection performance in adverse weather conditions?
Dual-domain strip attention for image restoration
c-yn/DSANet
•
•
Neural Networks 2024
In this paper, we develop a dual-domain strip attention mechanism for image restoration by enhancing representation learning, which consists of spatial and frequency strip attention units.
HIR-Diff: Unsupervised Hyperspectral Image Restoration Via Improved Diffusion Models
lipang/hirdiff
•
•
Specifically, the reduced image, which has a low spectral dimension, lies in the image field and can be inferred from our improved diffusion model where a new guidance function with total variation (TV) prior is designed to ensure that the reduced image can be well sampled.
MambaIR: A Simple Baseline for Image Restoration with State-Space Model
csguoh/mambair
•
•
In this way, our MambaIR takes advantage of the local pixel similarity and reduces the channel redundancy.
