Optimization of microscopy image compression using convolutional neural networks and removal of artifacts by deep generative adversarial networks

Nowadays, microscopy images are significant in medical research and clinical studies. However, storage and transmission of data such as microscopy images are challenging. Microscopy image compression is a vital area of digital microscope imaging in which image processing approaches are applied to capture the image by the microscope. It becomes accessible to interface the microscope to an image processing system because of technical advances in the microscope. Multiple application areas of microscope imaging, namely cancer research, drug testing, metallurgy, medicine, biological research, test-tube baby, etc., need microscopy image processing for analysis purposes. The microscopy image compression leads to complicated compression artifacts, like contouring, blocking, and ringing artifacts. Due to this problem, we select optimized Convolution Neural Networks (optimized-CNN), followed by Deep generative adversarial networks Deep-GAN, as a solution to reduce diverse compression artifacts. This research covers the compression of microscopy images and the removal of artifacts from a compressed microscopy image Optimized-CNN Deep-GAN based on Optimized-CNN and Deep-GAN. The concept of microscope image acquisition techniques and their analysis is also discussed. The performance of the Optimized-CNN Deep-GAN approach is measured using Peak Signal to Noise Ratio(PSNR), Compression Ratio(CR), Structural Similarity Index Measurement(SSIM), and Blind/Reference less Image Spatial Quality Evaluator(BRISQUE) and differentiated with state-of-the-art techniques. The experimental outcomes indicate the Optimized-CNN Deep-GAN technique acquires higher SSIM, BRISQUE, reduced space complexity, and better image quality than the existing image compression system. The proposed new model achieved CR 13.88, PSNR 40.6799 (dB), SSIM 0.9541, and BRISQUE 18.7645 values.