Modern methods of image quality enhancement in intrascopic medical imaging: comparative analysis and development trends

2025

Full text Open access: Green Metadata (CSL-JSON)
DOI 10.1117/12.3099346
Data confidence 70 medium
How this score is calculated

It is built from checkable properties of the record itself: whether the article has a DOI, how many independent sources confirmed its data, whether the required fields are filled in, and how recent the latest confirmation is. Disagreement between sources and a missing identifier lower it. A value of 80 or above means the record was published without further checking; 60 to 79 means it is published at reduced confidence, with a manual check advisable. A record verified by an editor by hand is never pushed below 90 by a recalculation. This scores our data about the article, not the article itself.

Abstract

This article presents a systematic review of modern methods for enhancing image quality in intrascopic diagnostic systems. It discusses the technical aspects of intrascopy, classifies image enhancement algorithms, and analyzes the role of artificial intelligence and deep learning. A comparative analysis of traditional and AI-based methods is provided, along with an outlook on emerging technologies.