Image Restoration
464 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
Step-Calibrated Diffusion for Biomedical Optical Image Restoration
mlneurosurg/truncated_diffusion
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Here, we present Restorative Step-Calibrated Diffusion (RSCD), an unpaired image restoration method that views the image restoration problem as completing the finishing steps of a diffusion-based image generation task.
DeblurDiNAT: A Lightweight and Effective Transformer for Image Deblurring
To this end, we propose DeblurDiNAT, a compact encoder-decoder Transformer which efficiently restores clean images from real-world blurry ones.
VmambaIR: Visual State Space Model for Image Restoration
To address these challenges, we propose VmambaIR, which introduces State Space Models (SSMs) with linear complexity into comprehensive image restoration tasks.
Selective Hourglass Mapping for Universal Image Restoration Based on Diffusion Model
Universal image restoration is a practical and potential computer vision task for real-world applications.
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
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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
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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
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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.
