Abstract Single Image Super Resolution (SISR) is a process to obtain a high pixel density
and refined details from a low resolution (LR) image to get upscaled and sharper high …

Objective measures of image quality generally operate by comparing pixels of a “degraded”
image to those of the original. Relative to human observers, these measures are overly …

The'bionic eye'—so long a dream of the future—is finally becoming a reality with retinal
prostheses available to patients in both the US and Europe. However, clinical experience …

We describe an image compression method, consisting of a nonlinear analysis
transformation, a uniform quantizer, and a nonlinear synthesis transformation. The …

Single image super-resolution is the task of inferring a high-resolution image from a single
low-resolution input. Traditionally, the performance of algorithms for this task is measured …

We propose a new approach to the problem of optimizing autoencoders for lossy image
compression. New media formats, changing hardware technology, as well as diverse …

This paper outlines an end-to-end optimized lossy image compression framework using
diffusion generative models. The approach relies on the transform coding paradigm, where …

In this paper, we propose an image quality transformer (IQT) that successfully applies a
transformer architecture to a perceptual full-reference image quality assessment (IQA) task …

Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed
remarkable progress with deep neural networks. Inspired by the characteristics of the human …

Image super-resolution (SR) is an underdetermined inverse problem, where a large number
of plausible high-resolution images can explain the same downsampled image. Most current …