Histopathology Image Enhancement Using Multi-Resolution Deep Learning Techniques

Accurate analysis of histopathology images is essential for disease diagnosis and treatment planning. However, the quality of digital pathology slides is often limited by scanner resolution, which can compromise diagnostic precision and patient care. To address this challenge, we conducted a comparative study evaluating four state of the art image enhancement methods: real enhanced super resolution generative adversarial network (Real-ESRGAN), SwinIR, multi scale image restoration network v2 (MIRNet-v2) and super resolution CNN (SRCNN). Our assessment focused on both quantitative metrics peak signal to noise ratio (PSNR) and structural similarity index (SSIM) and qualitative visual analysis to evaluate detail preservation. The experimental results revealed that SwinIR achieved the best quantitative performance among all evaluated methods, attaining the highest PSNR (35.81) and SSIM (0.95) for lung images from the LC2500 dataset at a 2 $\times$ upscaling factor. In contrast, real-ESRGAN excelled in perceptual quality, preserving finer image details more effectively, though it recorded slightly lower numerical scores (PSNR: 33.53, SSIM: 0.92) on the same dataset. These outcomes highlight essential trade off between perceptual fidelity and reconstruction quality, indicating that the optimal choice of enhancement method may vary depending on clinical or diagnostic priorities. The MIRNetv2 method delivered reasonable performance but ranked below both real-ESRGAN and SwinIR. Specifically, it achieved PSNR/SSIM scores of 30.67/0.94 on PR-IHC patches, 32.90/0.95 on lung images, and 31.87/0.95 on colon images, while scoring 29.11 for PR-IHC images in a separate evaluation. SRCNN demonstrated a balanced performance across datasets, achieving PSNR/SSIM values of 31.45/0.88 for lung images, 30.76/0.87 for PR-IHC patches, 32.62/0.93 for colon images, and 33.76/0.91 for PR-IHC. These findings underscore the real ESRGAN as the most effective method for improving the resolution and quality of histopathology images, supporting its potential integration into digital pathology workflows to enhance diagnostic accuracy and patient outcomes.