Multiple imaging modalities can be combined to provide more information about the real
world than a single modality alone. Infrared images discriminate targets with respect to their …

Multidimensional optical imaging has seen remarkable growth in the past decade. Rather
than measuring only the two-dimensional spatial distribution of light, as in conventional …

Hyperspectral image (HSI) reconstruction aims to recover the 3D spatial-spectral signal from
a 2D measurement in the coded aperture snapshot spectral imaging (CASSI) system. The …

Many learning-based algorithms have been developed to solve the inverse problem of
coded aperture snapshot spectral imaging (CASSI). However, CNN-based methods show …

Coded aperture snapshot spectral imaging (CASSI) is an effective tool to capture real-world
3D hyperspectral images. While a number of existing work has been conducted for …

We propose the l-net, which reconstructs hyperspectral images (eg, with 24 spectral
channels) from a single shot measurement. This task is usually termed snapshot …

Hyperspectral recovery from a single RGB image has seen a great improvement with the
development of deep convolutional neural networks (CNNs). In this paper, we propose two …

Snapshot compressive imaging (SCI) systems have been developed to capture high-
dimensional (> 3) signals using low-dimensional off-the-shelf sensors, ie, mapping multiple …

We propose a plug-and-play (PnP) method that uses deep-learning-based denoisers as
regularization priors for spectral snapshot compressive imaging (SCI). Our method is …

Regularization is a fundamental technique to solve an ill-posed optimization problem
robustly and is essential to reconstruct compressive hyperspectral images. Various hand …