An Improved OCT Image Denoising and Fusion Algorithm for Speckle Noise Based on Statistical Measures

This paper presents an improved Optical coherence tomography (OCT) image denoising and fusion algorithm by integrating statistical measures with data analysis techniques. OCT images are often degraded by speckle noise, which obscures structural details and impedes diagnostic accuracy. The method effectively suppresses speckle noise while preserving essential structural details, enhancing overall image quality. The proposed algorithm employs two complementary prefiltering strategies to generate optimized inputs for subsequent processing. And we leverage JS divergence for optimal weight allocation in low-frequency subband fusion, integrate mean, median, and mode disparities of high-frequency subband, dynamically adjusts weights to emphasize clinically relevant edges and textures. Experimental results confirm the robustness and effectiveness of our approach in balancing noise suppression and detail preservation. This research provides a reliable denoising framework that improves the quality of OCT images, contributing to more accurate medical diagnostics and expanding the potential applications of OCT imaging.