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
STAIC regularization for spatio-temporal image reconstruction
We propose a regularization-based image restoration scheme for 2D images recorded over time (2D+t).
Empowering Image Recovery_ A Multi-Attention Approach
Integration of attention mechanisms across feature and positional dimensions further enhances the recovery of fine details.
DiffBody: Human Body Restoration by Imagining with Generative Diffusion Prior
Human body restoration plays a vital role in various applications related to the human body.
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration
The network is a simple shallow network with an efficient block that implements global additive multidimensional averaging operations.
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions
Recent advances have demonstrated the powerful capability of transformer architecture in image restoration.
Towards Image Ambient Lighting Normalization
However, existing works often simplify this task within the context of shadow removal, limiting the light sources to one and oversimplifying the scene, thus excluding complex self-shadows and restricting surface classes to smooth ones.
Serpent: Scalable and Efficient Image Restoration via Multi-scale Structured State Space Models
The landscape of computational building blocks of efficient image restoration architectures is dominated by a combination of convolutional processing and various attention mechanisms.
Distilling Semantic Priors from SAM to Efficient Image Restoration Models
SPD leverages a self-distillation manner to distill the fused semantic priors to boost the performance of original IR models.
Latent Neural Cellular Automata for Resource-Efficient Image Restoration
Neural cellular automata represent an evolution of the traditional cellular automata model, enhanced by the integration of a deep learning-based transition function.
Osmosis: RGBD Diffusion Prior for Underwater Image Restoration
Using this prior together with a novel guidance method based on the underwater image formation model, we generate posterior samples of clean images, removing the water effects.
