A No-Reference Measure for Uneven Illumination Assessment on Laparoscopic Images
Tan-Sy Nguyen, John Chaussard, Marie Luong, Hatem Zaag, Azeddine Beghdadi · 2022 IEEE International Conference on Image Processing (ICIP) · 2022
A frequent degradation in video-guided surgery and especially laparoscopic and endoscopic surgery is uneven illumination, due in large part to physical limitations of the sensors and uncontrolled lighting conditions in the internal structure of the digestive tract and particularly at the level of the intestines. Surgical as well as postoperative task accuracy can be seriously affected by the perceptual quality of the acquired images or videos. In this respect, a No-Reference Image Quality Assessment (NR-IQA) metric dedicated to uneven illumination is proposed in this paper. The key idea is to analyze the effect of contrast enhancement on the spatial distribution of the luminance component of the signal. The results obtained through extensive experiments, performed on a challenging dedicated database, have shown that the proposed metric significantly improves the state-of-the-art NR-IQA metrics when applied to this type of video content and distortion.