# Gastrointestinal image stitching based on improved unsupervised algorithm

## TL;DR

This paper introduces an improved unsupervised image stitching method to enhance gastroenteroscopy by increasing the field of view and reducing missed detections.

## Contribution

The novel contribution is an improved unsupervised framework with preprocessing and a C2f module for better feature extraction in gastrointestinal image stitching.

## Key findings

- The proposed method improves stitching metrics like MSE, RMSE, PSNR, and SSIM.
- A new dataset, GASE-Dataset, is introduced for benchmarking unsupervised gastrointestinal image stitching.
- The method outperforms traditional techniques while maintaining acceptable stitching time.

## Abstract

Image stitching is a traditional but challenging computer vision task. The goal is to stitch together multiple images with overlapping areas into a single, natural-looking, high-resolution image without ghosts or seams. This article aims to increase the field of view of gastroenteroscopy and reduce the missed detection rate. To this end, an improved depth framework based on unsupervised panoramic image stitching of the gastrointestinal tract is proposed. In addition, preprocessing for aberration correction of monocular endoscope images is introduced, and a C2f module is added to the image reconstruction network to improve the network’s ability to extract features. A comprehensive real image data set, GASE-Dataset, is proposed to establish an evaluation benchmark and training learning framework for unsupervised deep gastrointestinal image splicing. Experimental results show that the MSE, RMSE, PSNR, SSIM and RMSE_SW indicators are improved, while the splicing time remains within an acceptable range. Compared with traditional image stitching methods, the performance of this method is enhanced. In addition, improvements are proposed to address the problems of lack of annotated data, insufficient generalization ability and insufficient comprehensive performance in image stitching schemes based on supervised learning. These improvements provide valuable aids in gastrointestinal examination.