Adaptive degradation-aware medical image enhancement for multi-modal diagnostics

Abstract

Medical image enhancement plays a vital role in the rapid development of medical technology, focusing on improving medical image quality and clarity to support accurate diagnosis and effective treatment. However, poor medical imaging, such as imbalanced intensity or non-uniform illumination, brings significant challenges to automated diagnosis analysis and screening of diseases. This paper proposes a novel adaptive degradation-aware medical image enhancement to improve the quality of poorly illuminated medical images captured under a low-light enclosed intestinal environment while conserving the critical pathological details. In this work, a novel complementary illumination adjustment function is employed to improve the brightness of dark regions while preventing overexposure in bright areas. Then, the proposed method incorporates a guided and bilateral filter to improve delicate clinical details and anatomical structures while suppressing contrast degradation of medical images. Experiment results comprehensively illustrate the performance of contrast enhancement in clinical diagnosis by effectively preserving color information and structure details. Extensive experimental evaluation demonstrates the superior performance of the proposed method in terms of qualitative and different quantitative metrics compared to other recent existing methods.