CDMSP: a convolutional dense multi-scale pooling framework for low-light colonoscopy image enhancement

Colorectal cancer (CRC) remains one of the main cancer-related causes of death worldwide, and colonoscopy is an effective method for early identification of CRC and substantially reducing the risk. Colonoscopy images are often affected by non-uniform illumination and low-light conditions, which make it difficult to visualize mucosal textures and lesion margins. In this study, we introduce a low-light colonoscopy image enhancement framework, Convolutional Dense Attention Network (CDAN)-Dense- Multi-Scale Pooling (MSP) (CDMSP), to address this problem. The framework centers around a CDAN, which is constructed through dense connections and attention mechanisms to increase feature propagation and emphasize diagnostically important areas while reducing noise and lighting artifacts. In addition, a small MSP module is used to capture contextual information at different spatial scales, thereby helping preserve the details of the structures and edges. The overall loss function, which is a combination of structural similarity, perceptual similarity, edge-based contrast, and color fidelity, is used to maintain a balance between contrast enhancement and the preservation of anatomy. According to the experimental results, the proposed method achieved a Structural Similarity Index Measure (SSIM) of 0.8757, a Learned Perceptual Image Patch Similarity (LPIPS) of 0.0742, an edge-based contrast measure (EBCM) of 0.5601, and a Color Fidelity Index (CFI) of 95.1760. It is extremely efficient with an inference time of [23.3 milliseconds (ms), 42.99 frames per second (FPS)], making it suitable for real-time clinical use.