Short-and-sparse deconvolution (SaSD) is the problem of extracting localized, recurring
motifs in signals with spatial or temporal structure. Variants of this problem arise in …

Automated nucleus/cell detection is usually considered as the basis and a critical
prerequisite step of computer assisted pathology and microscopy image analysis. However …

The transformer oil's insulation effect has a great impact on the safety of transformers.
However, during the long-term operation of the transformer, the aging of the insulating paper …

Over the past decade, the celebrated sparse representation model has achieved impressive
results in various signal and image processing tasks. A convolutional version of this model …

Sparse dictionary learning is a popular method for representing signals as linear
combinations of a few elements from a dictionary that is learned from the data. In the …

Convolutional dictionary learning (CDL) estimates shift invariant basis adapted to represent
signals or images. CDL has proven useful for image denoising or inpainting, as well as for …

The growing usage of digital image processing practices devoted to health is explicit, aiding
in the solution, improving diagnosis, and creating new diagnostic schemes …

An efficient product packing system through a simplified automatic counting and collecting
process is a critical aspect of the manufacturing industry. In this article, we propose a real …

In this paper, we consider the task of detecting platelets in images of diluted whole blood
taken with a lens-free microscope. Despite having several advantages over traditional …

In-line lensless digital holography has great potential in multiple applications; however,
reconstructing high-quality images from a single recorded hologram is challenging due to …