The sparse prior has been widely adopted to establish data models for numerous
applications. In this context, most of them are based on one of three foundational paradigms …

To support high-level analysis of spaceborne imaging spectroscopy (hyperspectral)
imagery, spectral unmixing has been gaining significance in recent years. However, from the …

Gesture recognition becomes a popular analytics tool for extracting the characteristics of
user movement and enables numerous practical applications in the biometrics field. Despite …

Recently, sparse coding-based image fusion methods have been developed extensively.
Although most of them can produce competitive fusion results, three issues need to be …

The high spectral resolution of hyperspectral image (HSI) provides the possibility to capture
the subtle changes associated with land-cover dynamic evolution process. Supervised deep …

This paper established a relationship between multi-spectral palm images and a face image
based on multiple fusion methods. The first fusion method to be considered is a feature …

The iterative shrinkage-thresholding algorithm (ISTA) is often used to address the
convolutional dictionary learning problem. However, the ISTA algorithm is easy to fall into …

Abstract Differentiable Architecture Search (DARTS) improves the efficiency of architecture
search by learning the architecture and network parameters end-to-end. However, the …

Convolutional dictionary learning (CDL) aims to learn a structured local convolutional
dictionary and the sparse coefficient maps from the signals of various of interest, achieving …

Conventional learning methods simplify the bilinear model by regarding two intrinsically
coupled factors independently, which degrades the optimization procedure. One reason lies …