A Clinical-oriented Lightweight Network for High-resolution Medical Image Enhancement

Medical images captured in less-than-optimal conditions may suffer from quality degradation, such as blur, artifacts, and low lighting, which potentially leads to misdiagnosis. Unfortunately, state-of-the-art medical image enhancement methods face challenges in both high-resolution image quality enhancement and local distinct anatomical structure preservation. To address these issues, we propose a Clinical-oriented High-resolution Lightweight Medical Image Enhancement Network, termed CHLNet, which proficiently addresses high-resolution medical image enhancement, detailed pathological characteristics, and lightweight network design simultaneously. More specifically, CHLNet comprises two main components: 1) High-resolution Assisted Quality Enhancement Network for removing global low-quality factors in high-resolution images thus enhancing overall image quality; 2) High-quality-semantic Guided Quality Enhancement Network for capturing semantic knowledge from high-quality images such that detailed structure preservation is enforced. Moreover, thanks to its lightweight design, CHLNet can be easily deployed on medical edge devices. Extensive experiments on three public medical image datasets demonstrate the effectiveness and superiority of CHLNet over the state-of-the-art.