Enhancing Medical Imaging: Noise Reduction and Super Resolution with Transfer Learning

Medical image improvement, including noise reduction and super-resolution, is vital for proper diagnosis and therapy. However, conventional approaches typically fail to generate sufficient results, particularly in the presence of both noise and poor resolution. Leveraging the power of pre-trained models, MAXIM for noise reduction, and ESRGAN for solving large-scale problems, our methods combine their strengths to provide unique results. MAXIM denoiser intelligently reduces noise to provide clean input to ESRGAN super-resolution models. Unlike traditional cascade systems, our integrated system allows for contact and flexibility, allowing for simultaneous optimization. The proposed method has been carefully evaluated on STARE (Structured Analysis of the Retina) and NIH Chest X-ray datasets and has been found to provide significant improvements in image quality compared to separate methods. Quantitative measurements, including PSNR and SSIM, supported the performance of the combined model. Comparative analysis with existing models, including SRGAN, SRCNN, and ESRGAN, demonstrates the effectiveness of our approach. This research is an important contribution to the improvement of medical images in terms of noise reduction and super-resolution to facilitate better evaluation and analysis.