A Novel Approach to Super-Resolution and Denoising: Refining Medical Image quality and Diagnostic Textual

One of the promising recent advances in medical imaging is indeed the successful exploration of GANs for applications such as image super-resolution and text enhancement. Most GAN-based approaches, however, have been driven for a single modality: enhancing the quality of images or text clarity-while simultaneously eliciting little attention towards the two interrelated aspects. A new dual- GAN framework is therefore proposed to overcome this limitation by combining the merits of both Image Super- Resolution GAN and Text Enhancement GAN. In this merged approach, both the resolution of images and the adequacy of the textual annotation improved to enhance diagnostic accuracy as well as improve the visualization of small lesions and tissue structures in medical images. The system works on the principle of cross-domain benefit sharing; enhancement in either quality, the image, or the text, naturally boosts the quality of the other. The performance of the proposed dual-GAN system is measured through some key metrics, such as Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM) and Optical Character Recognition (OCR) accuracy to provide clear evidence of significant improvement in both image and text qualities as compared with other conventional methods. This innovation improves the process of clinical decision-making in the sense that clearer images and text are more accurate. The approach has tremendous potential for much wider practical applications: document scanning, surveillance, astronomy, and microscopy-all applications where textual and visual clarity are important for precision and analysis.