About
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).
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Latest papers without code
Perceptual Image Restoration with High-Quality Priori and Degradation Learning
We show that our model performs well in measuring the similarity between restored and degraded images.
Deblurring Processor for Motion-Blurred Faces Based on Generative Adversarial Networks
Then we conducted a motion blur image generation experiment on some general facial data set, and used the pairs of blurred and sharp face image data to perform the training and testing experiments of the processor GAN, and gave some visual displays.
DEBLURRING FACE DETECTION FACE RECOGNITION IMAGE GENERATION IMAGE RESTORATION
Feature-Align Network and Knowledge Distillation for Efficient Denoising
Deep learning-based RAW image denoising is a quintessential problem in image restoration.
Deep Unrolled Network for Video Super-Resolution
Yet, they fail to incorporate some knowledge about the image formation model, which limits their flexibility.
A GAN-Based Input-Size Flexibility Model for Single Image Dehazing
Based on the atmospheric scattering model, we design a novel model to directly generate the haze-free image.
IMAGE DEHAZING IMAGE RESTORATION IMAGE-TO-IMAGE TRANSLATION SINGLE IMAGE DEHAZING
Learning local regularization for variational image restoration
In this work, we propose a framework to learn a local regularization model for solving general image restoration problems.
Learning to Enhance Visual Quality via Hyperspectral Domain Mapping
We incorporate a self-supervision and a spectral profile regularization network to infer a plausible HSI from an RGB image.
Deep Iteration Assisted by Multi-level Obey-pixel Network Discriminator (DIAMOND) for Medical Image Recovery
Image restoration is a typical ill-posed problem, and it contains various tasks.
Image Restoration by Deep Projected GSURE
In the first one, where no explicit prior is used, we show that the proposed approach outperforms other internal learning methods, such as DIP.
Parallax estimation for push-frame satellite imagery: application to super-resolution and 3D surface modeling from Skysat products
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.
IMAGE RESTORATION MULTI-FRAME SUPER-RESOLUTION OPTICAL FLOW ESTIMATION