Microscopic Muscle Image Enhancement

TL;DR

This paper introduces a specialized, efficient, and parameter-free image enhancement algorithm for muscle fiber images captured by microscopes, addressing blur and out-of-focus issues with a novel non-uniform blind deblurring approach.

Contribution

A new spatially non-uniform blind deblurring framework tailored for muscle images, overcoming limitations of traditional methods and improving image quality.

Findings

Effective in reducing blur and artifacts in muscle images

Outperforms traditional deconvolution methods on muscle microscopy data

Computationally efficient and easy to use

Abstract

We propose a robust image enhancement algorithm dedicated for muscle fiber specimen images captured by optical microscopes. Blur or out of focus problems are prevalent in muscle images during the image acquisition stage. Traditional image deconvolution methods do not work since they assume the blur kernels are known and also produce ring artifacts. We provide a compact framework which involves a novel spatially non-uniform blind deblurring approach specialized to muscle images which automatically detects and alleviates degraded regions. Ring artifacts problems are addressed and a kernel propagation strategy is proposed to speedup the algorithm and deals with the high non-uniformity of the blur kernels on muscle images. Experiments show that the proposed framework performs well on muscle images taken with modern advanced optical microscopes. Our framework is free of laborious parameter settings and is computationally efficient.