EndoCaver: Handling Fog, Blur and Glare in Endoscopic Images via Joint Deblurring-Segmentation

TL;DR

EndoCaver is a lightweight transformer model that jointly performs deblurring and segmentation of endoscopic images, improving accuracy under challenging conditions while being suitable for on-device clinical use.

Contribution

It introduces a novel joint deblurring-segmentation transformer architecture with reduced complexity and state-of-the-art performance on endoscopic image datasets.

Findings

Achieves 0.922 Dice on clean data and 0.889 under severe degradation

Reduces model parameters by 90% compared to previous methods

Outperforms state-of-the-art in robustness and efficiency

Abstract

Endoscopic image analysis is vital for colorectal cancer screening, yet real-world conditions often suffer from lens fogging, motion blur, and specular highlights, which severely compromise automated polyp detection. We propose EndoCaver, a lightweight transformer with a unidirectional-guided dual-decoder architecture, enabling joint multi-task capability for image deblurring and segmentation while significantly reducing computational complexity and model parameters. Specifically, it integrates a Global Attention Module (GAM) for cross-scale aggregation, a Deblurring-Segmentation Aligner (DSA) to transfer restoration cues, and a cosine-based scheduler (LoCoS) for stable multi-task optimisation. Experiments on the Kvasir-SEG dataset show that EndoCaver achieves 0.922 Dice on clean data and 0.889 under severe image degradation, surpassing state-of-the-art methods while reducing model parameters by 90%. These results demonstrate its efficiency and robustness, making it well-suited for on-device clinical deployment. Code is available at https://github.com/ReaganWu/EndoCaver.