Smoke removal and image enhancement of laparoscopic images by an artificial multi-exposure image fusion method

In laparoscopic surgery, image quality is often degraded by surgical smoke or side effects of the illumination system, such as reflections, specularities, and non-uniform illumination. The images complicate work surgeons may lead to errors in image-guided surgery. Existing enhancement algorithms mainly focus on enhancing global contrast, overlooking local contrast. Here, we propose a new Patch Adaptive Structure Decomposition utilizing Multi-Exposure Fusion technique enhance contrast for better visualization. set under-exposure level obtained from single input blurred using gamma correction. Spatial linear saturation applied adjust saturation. (MEF) used series multi-exposure obtain clear smoke-free fused image. MEF adaptive structure decomposition all patches. Image entropy based texture energy calculate strength. determined patch size that useful structure. proposed method effectively eliminate images. qualitative results showed visual resultant improved smoke-free. Furthermore, quantitative scores computed metrics: FADE, Blur, JNBM, Edge Intensity are significantly compared other existing methods.