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
Dynamic Pre-training: Towards Efficient and Scalable All-in-One Image Restoration
All-in-one image restoration tackles different types of degradations with a unified model instead of having task-specific, non-generic models for each degradation.
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
KU-CVLAB/Perturbed-Attention-Guidance
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These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration.
SeNM-VAE: Semi-Supervised Noise Modeling with Hierarchical Variational Autoencoder
We employ our method to generate paired training samples for real-world image denoising and super-resolution tasks.
Omni-Kernel Network for Image Restoration
c-yn/OKNet
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Proceedings of the AAAI Conference on Artificial Intelligence 2024
Extensive experiments demonstrate that our network achieves state-of-the-art performance on 11 benchmark datasets for three representative image restoration tasks, including image dehazing, image desnowing, and image defocus deblurring.
Graph Image Prior for Unsupervised Dynamic MRI Reconstruction
The inductive bias of the convolutional neural network (CNN) can act as a strong prior for image restoration, which is known as the Deep Image Prior (DIP).
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation
c-yn/adair
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Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task.
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.
