Motion Quantification and Automated Correction in Clinical RSOM Using CNN-Based Dehazing

Motion Quantification and Automated Correction in Clinical RSOM Using CNN-Based Dehazing is a medical imaging system designed to improve RSOM image quality by reducing motion artifacts. Patient movement during RSOM scanning often causes blurred and low-contrast images, affecting diagnostic accuracy. The proposed system applies spatio-temporal motion estimation to detect motion patterns and uses a CNN-based dehazing model to correct intensity distortions. Inspired by classical haze removal and deep learning techniques, the model reconstructs clearer and high-contrast images. This integration of motion analysis and deep learning enhances vessel visibility and improves the reliability of clinical RSOM imaging