Multi-frequency and Smoke Attention-Aware Learning Based Diffusion Model for Removing Surgical Smoke

Surgical smoke in laparoscopic surgery can deteriorate the visibility and pose hazards to surgeons, although medical devices for mechanical smoke evacuation worked well, its prolonged operative duration and thus restricted the efficiency. This work aims to simultaneously remove the surgical smoke and restore the true-to-live image colors with deep learning strategy to improve the surgical efficiency and safety. However, the deep network-based smoke removal remains a challenge due to: 1) higher frequency modes are hindered from being learned by spectral bias, 2) the distribution of surgical smoke is non-homogeneity. We propose the multi-frequency and smoke attention-aware learning-based diffusion model for removing surgical smoke. In this work, the frequency compensation strategy combines the multi-level frequency learning and contrast enhancement to integrates comprehensive features for learning mid-to-high frequency details that the smoke has obscured. The smoke attention learning employs the pixel-wise measurement and provides the diffusion model with complementary features about where smoke is present, which helps restore the smokeless regions during the inverse diffusion process. And the multi-task learning strategy incorporates \(L_1\) loss, smoke perception loss, dark channel prior loss, and contrast enhancement loss to help the model optimization. Additionally, a paired smokeless/smoky dataset is simulated by a 3D smoke rendering engine. The experimental results show that the proposed method outperforms other state-of-the-art methods on both synthetic/real laparoscopic surgical images, with the potential to be embedded in laparoscopic devices for smoke removal.