Novel denoising technique for optical coherence tomography images

This study introduces a learning-assisted denoising technique for skin Optical Coherence Tomography (OCT) images. By combining Reinforcement Learning (RL) with the Denoising Convolutional Neural Network (DnCNN), we achieve enhanced denoising capabilities. The method iteratively refines DnCNN parameters through RL-guided policies, demonstrating superior performance. Tailored for skin OCT images, the approach prioritizes preserving vital structures for accurate clinical assessments. This integration of RL into DnCNN training represents a promising advancement in medical image denoising, particularly for dermatological diagnostics.