As ChatGPT goes viral, generative AI (AIGC, aka AI-generated content) has made headlines
everywhere because of its ability to analyze and create text, images, and beyond. With such …

This paper reviews the first challenge on single image super-resolution (restoration of rich
details in an low resolution image) with focus on proposed solutions and results. A new …

Image deblurring is a classic problem in low-level computer vision with the aim to recover a
sharp image from a blurred input image. Advances in deep learning have led to significant …

Numerous learning-based approaches to single image deblurring for camera and object
motion blurs have recently been proposed. To generalize such approaches to real-world …

This paper develops a Bayesian retinex algorithm for enhancing single underwater image
with multiorder gradient priors of reflectance and illumination. First, a simple yet effective …

Existing deep learning methods for image deblurring typically train models using pairs of
sharp images and their blurred counterparts. However, synthetically blurring images does …

Despite deep end-to-end learning methods have shown their superiority in removing non-
uniform motion blur, there still exist major challenges with the current multi-scale and scale …

In single image deblurring, the``coarse-to-fine''scheme, ie gradually restoring the sharp
image on different resolutions in a pyramid, is very successful in both traditional optimization …

Planar digital image sensors facilitate broad applications in a wide range of areas,,,–, and
the number of pixels has scaled up rapidly in recent years,. However, the practical …

We present DeblurGAN, an end-to-end learned method for motion deblurring. The learning
is based on a conditional GAN and the content loss. DeblurGAN achieves state-of-the art …