# Residual Networks based Distortion Classification and Ranking for Laparoscopic Image Quality Assessment

arXiv: https://arxiv.org/abs/2106.06784 · HTML: https://mlsanity.com/paper/2106.06784/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 22 references that are in the map.
- First posted on arXiv 12 Jun 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [NIMA: Neural Image Assessment](https://mlsanity.com/paper/1709.05424/) (2017)
- [EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos](https://mlsanity.com/paper/1602.03012/) (2016)
- [RankIQA: Learning from Rankings for No-reference Image Quality Assessment](https://mlsanity.com/paper/1707.08347/) (2017)
- [Learning to Rank for Blind Image Quality Assessment](https://mlsanity.com/paper/1309.0213/) (2013)
- [Towards a Video Quality Assessment based Framework for Enhancement of Laparoscopic Videos](https://mlsanity.com/paper/2003.12679/) (2020)

## Built on by (most cited first, 3 of 3)

- [A Neural Network based Framework for Effective Laparoscopic Video Quality Assessment](https://mlsanity.com/paper/2202.04517/) (2022)
- [CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision](https://mlsanity.com/paper/2311.06976/) (2023)
- [ExIQA: Explainable Image Quality Assessment Using Distortion Attributes](https://mlsanity.com/paper/2409.06853/) (2024)

## Abstract (arXiv)

Laparoscopic images and videos are often affected by different types of distortion like noise, smoke, blur and nonuniform illumination. Automatic detection of these distortions, followed generally by application of appropriate image quality enhancement methods, is critical to avoid errors during surgery. In this context, a crucial step involves an objective assessment of the image quality, which is a two-fold problem requiring both the classification of the distortion type affecting the image and the estimation of the severity level of that distortion. Unlike existing image quality measures which focus mainly on estimating a quality score, we propose in this paper to formulate the image quality assessment task as a multi-label classification problem taking into account both the type as well as the severity level (or rank) of distortions. Here, this problem is then solved by resorting to a deep neural networks based approach. The obtained results on a laparoscopic image dataset show the efficiency of the proposed approach.