While deep learning methods have achieved state-of-the-art performance in many
challenging inverse problems like image inpainting and super-resolution, they invariably …

Compressive image recovery is a challenging problem that requires fast and accurate
algorithms. Recently, neural networks have been applied to this problem with promising …

Compressed sensing (CS) and its medical applications are active areas of research. In this
paper, we review recent works using deep learning method to solve CS problem for images …

Lensless imaging has emerged as a potential solution towards realizing ultra-miniature
cameras by eschewing the bulky lens in a traditional camera. Without a focusing lens, the …

Traditional algorithms for compressive sensing recovery are computationally expensive and
are ineffective at low measurement rates. In this paper, we propose a data-driven …

With the development of deep learning technologies, recent research on real-world noisy
image denoising has achieved a considerable improvement in performance. However, a …

Fourier Ptychography is a recently proposed imaging technique that yields high-resolution
images by computationally transcending the diffraction blur of an optical system. At the crux …

Recent advancements in fields like Internet of Things (IoT), augmented reality, etc. have led
to an unprecedented demand for miniature cameras with low cost that can be integrated …

The emergence of the single-chip polarized color sensor now allows for simultaneously
capturing chromatic and polarimetric information of the scene on a monochromatic image …

We present a convolutional neural network architecture for performing joint design of color
filter array (CFA) patterns and demosaicing. Our generic model allows the training of CFAs …