Specular Reflections Detection and Removal for Endoscopic Images Based on Brightness Classification

Area of Science:

• Medical Imaging
• Computer Vision
• Image Processing

Background:

• Specular reflections in endoscopic images hinder computer vision algorithms and surgical judgment.
• Existing methods for highlight detection and removal are limited, especially for high-resolution medical images with complex textures.
• Current algorithms struggle with efficiency and effectiveness when dealing with large specular regions or varying image brightness.

Purpose of the Study:

• To propose an effective specular reflection detection and removal method for endoscopic images.
• To overcome the limitations of existing methods in handling varying brightness and complex textures.
• To improve the efficiency and accuracy of specular reflection processing in high-resolution medical images.

Main Methods:

• A brightness classification-based approach for specular reflection detection and removal in endoscopic images.
• Utilizes an adaptive threshold function for highlight detection, varying with image brightness.
• Modifies exemplar-based image inpainting with local priority computing and adaptive local search for efficient and accurate highlight recovery.

Main Results:

• The proposed method effectively detects specular regions across different image brightness levels.
• It successfully restores texture structure information in high-resolution images.
• Demonstrates superior qualitative and quantitative performance compared to state-of-the-art methods, with significantly improved algorithm efficiency.

Conclusions:

• The brightness classification-based method offers a robust solution for specular reflection challenges in endoscopic imaging.
• The approach enhances image analysis by improving clarity and reducing interference from reflections.
• Significant improvements in processing efficiency make it suitable for high-resolution medical image applications.