Convolutional sparse representations are a form of sparse representation with a dictionary
that has a structure that is equivalent to convolution with a set of linear filters. While effective …

Plug-and-play priors (PnP) is a powerful framework for regularizing imaging inverse
problems by using advanced denoisers within an iterative algorithm. Recent experimental …

Regularization by denoising (RED) is an image reconstruction framework that uses an
image denoiser as a prior. Recent work has shown the state-of-the-art performance of RED …

Spectral computed tomography (CT) has recently emerged as an advanced version of
medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two …

Convolutional neural networks (CNNs) have been tremendously successful in solving
imaging inverse problems. To understand their success, an effective strategy is to construct …

Convolutional sparse representations are a form of sparse representation with a structured,
translation-invariant dictionary. Most convolutional dictionary learning algorithms to date …

Convolutional sparse coding (CSC) improves sparse coding by learning a shift-invariant
dictionary from the data. However, most existing CSC algorithms operate in the batch mode …

As an important branch of information fusion, infrared–visible image fusion can generate
scene information with rich texture details via signal processing technology. The fused …

In this paper, we propose a noise-robust online convolutional coding model for image
representation, which can use the noisy images as training data. Then an alternating …

Objective. Dual-energy computed tomography (DECT) has the potential to improve contrast
and reduce artifacts and the ability to perform material decomposition in advanced imaging …