Here are the [PDF] Optimization of microscopy image compression using convolutional neural networks and removal of artifacts by deep generative adversarial networks journals presenting the latest research across various disciplines. From social sciences to technology, each article is expected to provide valuable insights to our readers.
Raj Kumar Paul1 · Dipankar Misra2 · Shibaprasad Sen3 · Saravanan Chandran1
Received: 9 February 2023 / Revised: 26 September 2023 / Accepted: 8 October 2023
© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023
Abstract
Nowadays, microscopy images are significant in medical research and clinical studies. How-
ever, 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 com-
plicated 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 compres-
sion 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 exper-
imental outcomes indicate the Optimized-CNN Deep-GAN technique acquires higher SSIM,
BRISQUE, reduced space complexity, and better image quality than the existing image com-
pression system. The proposed new model achieved CR 13.88, PSNR 40.6799 (dB), SSIM
0.9541, and BRISQUE 18.7645 values.
https://link.springer.com/article/10.1007/s11042-023-17494-0
https://doi.org/10.1007/s11042-023-17494-0