Microscopic Muscle Image Enhancement
This work addresses image quality challenges for researchers analyzing muscle images, though it appears incremental as it builds on existing deblurring methods with domain-specific adaptations.
The authors tackled the problem of blur and out-of-focus issues in muscle fiber specimen images captured by optical microscopes, proposing a robust image enhancement algorithm that automatically detects and alleviates degraded regions while addressing ring artifacts and improving computational efficiency.
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.