Significance Spectral imaging, which includes hyperspectral and multispectral imaging, can
provide images in numerous wavelength bands within and beyond the visible light …

Ideal imaging, which is constantly pursued, requires the collection of all kinds of optical
information of the objects in view, such as three-dimensional spatial information (3D) …

Spectral imaging collects and processes information along spatial and spectral coordinates
quantified in discrete voxels, which can be treated as a 3D spectral data cube. The spectral …

Multi-spectral imaging is a fundamental tool characterizing the constituent energy of scene
radiation. However, current multi-spectral video cameras cannot scale up beyond megapixel …

Hyperspectral imaging is useful for applications ranging from medical diagnostics to
agricultural crop monitoring; however, traditional scanning hyperspectral imagers are …

Imaging depth and spectrum have been extensively studied in isolation from each other for
decades. Recently, hyperspectral-depth (HS-D) imaging emerges to capture both …

We present a novel hyperspectral image reconstruction algorithm, which overcomes the
long-standing tradeoff between spectral accuracy and spatial resolution in existing …

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 …

Traditional snapshot hyperspectral imaging systems include various optical elements: a
dispersive optical element (prism), a coded aperture, several relay lenses, and an imaging …