Attention-aware network with lightness embedding and Hybrid Guided Embedding for laparoscopic image desmoking

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.