腹腔鏡画像のスモーク除去のための軽量埋め込みとハイブリッドガイド埋め込みを備えた注意機構ネットワーク

Surgical smoke removal is crucial for enhancing laparoscopic image quality in computer-assisted surgery. While existing methods utilize estimated smoke distribution to address non-homogeneous characteristics, most treat this information merely as prior input and often suffer from over-desmoking artifacts. To address these limitations, this study introduces a desmoking network that reconstructs smoke-free images by explicitly utilizing smoke distribution information. The network comprises two key modules: the Smoke Attention Estimator (SAE) and the Hybrid Guided Embedding (HGE). The SAE generates a smoke attention map via a channel-aware position embedding with lightness prior to improve accuracy. The HGE takes the predicted smoke attention map from the SAE as input and employs convolutional layers along with a novel field transformation method to generate residual terms. By combining these residual terms with the original image, the HGE preserves fine details in smoke-free regions, thereby preventing over-desmoking. Experimental results reveal that the proposed method achieves improvements of at least 3.71% in Peak Signal-to-Noise Ratio (PSNR) and 18.75% in Learned Perceptual Image Patch Similarity compared to state-of-the-art methods on the synthetic dataset, while attaining the lowest Perception-based Image Quality Evaluator score (24.55) on the Cholec80 dataset. It operates at around 174 frames per second, indicating strong real-time processing capability. The network achieves over 40 dB in PSNR for smoke-free regions, excelling in both color restoration and detail preservation. This work is available at https://homepage.hit.edu.cn/wpgao?lang=en.