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).
We show that our model performs well in measuring the similarity between restored and degraded images.
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.
We incorporate a self-supervision and a spectral profile regularization network to infer a plausible HSI from an RGB image.
Image restoration is a typical ill-posed problem, and it contains various tasks.
Recent constellations of satellites, including the Skysat constellation, are able to acquire bursts of images.