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Endoscopic video defogging using luminance blending

Xióngbiāo Luó, Fan Yang, Huiqing Zeng, Yanping Du

Year
2019
Citations
7
Access
Open access

Abstract

Endoscopic video sequences provide surgeons with direct surgical field or visualisation on anatomical targets in the patient during robotic surgery. Unfortunately, these video images are unavoidably hazy or foggy to prevent surgeons from clear surgical vision due to typical surgical operations such as ablation and cauterisation during surgery. This Letter aims at removing fog or smoke on endoscopic video sequences to enhance and maintain a direct and clear visualisation of the operating field during robotic surgery. The authors propose a new luminance blending framework that integrates contrast enhancement with visibility restoration for foggy endoscopic video processing. The proposed method was validated on clinical endoscopic videos that were collected from robotic surgery. The experimental results demonstrate that their method provides a promising means to effectively remove fog or smoke on endoscopic video images. In particular, the visual quality of defogged endoscopic images was improved from 0.5088 to 0.6475.

Keywords

Computer scienceComputer visionVisibilityArtificial intelligenceLuminanceVisualizationEndoscopic surgeryEndoscopySurgeryMedicine

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