In this letter, a sparse representation (SR) model named convolutional sparsity based
morphological component analysis (CS-MCA) is introduced for pixel-level medical image …

To effectively reduce JPEG compression artifacts, we propose a deep convolutional sparse
coding (DCSC) network architecture. We design our DCSC in the framework of classic …

Over the past few years, dictionary learning (DL)-based methods have been successfully
used in various image reconstruction problems. However, the traditional DL-based …

The recently proposed multilayer convolutional sparse coding (ML-CSC) model, consisting
of a cascade of convolutional sparse layers, provides a new interpretation of convolutional …

Deep learning-based methods have achieved notable progress in removing blocking
artifacts caused by lossy JPEG compression on images. However, most deep learning …

Interferometric phase restoration has been investigated for decades and most of the state-of-
the-art methods have achieved promising performances for InSAR phase restoration. These …

Technology-assisted clinical diagnosis has gained tremendous importance in modern day
healthcare systems. To this end, multimodal medical image fusion has gained great …

Image smoothing is a prerequisite for many computer vision and graphics applications. In
this article, we raise an intriguing question whether a dataset that semantically describes …

When people take a picture through glass, the scene behind the glass is often interfered by
specular reflection. Due to relatively easy implementation, most studies have tried to recover …

Spectral computed tomography (CT) reconstructs images from different spectral data
through photon counting detectors (PCDs). However, due to the limited number of photons …