named as MIRNet, achieves state-of-the-art results for a variety of image processing tasks, including image denoising, super-resolution and image enhancement.'
author:
- Syed Waqas Zamir
- Aditya Arora
- Salman Khan
- Munawar Hayat
- Fahad Shahbaz Khan
- 'Ming-Hsuan Yang'
- Ling Shao
bibliography:
- 'MIRNet.bib'
title: Learning Enriched Features for Real Image Restoration and Enhancement
---

Introduction
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Image content is exponentially growing due to the ubiquitous presence of cameras on various devices. During image acquisition, degradations of different severities are often introduced. It is either because of the physical limitations of cameras, or due to inappropriate lighting conditions. For instance, smart phone cameras come with narrow aperture, and have small sensors with limited dynamic range. Consequently, they frequently generate noisy and low-contrast images. Similarly, images captured under the unsuitable lighting are either too dark, or too bright. The art of recovering the original clean image from its corrupted measurements is studied under the image restoration task. It is an ill-posed inverse problem, due to the existence of many possible solutions.

Recently, deep learning models have made significant advancements for image restoration and enhancement, as they can learn strong (generalizable) priors from large-scale datasets. Existing CNNs typically follow 