In the past two decades or so, there has been a renaissance of optical microscopy research
and development. Much work has been done in an effort to improve the resolution and …

Traditional optical imaging and sensing methods capture signals of interest by direct
sampling in the domain of interest such as by forming images on pixelated camera sensors …

In this paper, we present a novel technique, based on compressive sensing principles, for
reconstruction and enhancement of multi-dimensional image data. Our method is a major …

In this paper, we make contact with the field of compressive sensing and present a
development and generalization of tools and results for reconstructing irregularly sampled …

We demonstrate the usefulness of utilizing a segmentation step for improving the
performance of sparsity based image reconstruction algorithms. In specific, we will focus on …

Deep learning for image denoising has recently attracted considerable attentions due to its
excellent performance. Since most of current deep learning-based denoising models require …

The biological nervous system possesses a powerful information processing capability, and
only needs a partial signal stimulation to perceive the entire signal. Likewise, the hardware …

We present a nonlocal weighted sparse representation (NWSR) method for reconstruction of
retinal optical coherence tomography (OCT) images. To reconstruct a high signal-to-noise …

We study the problem of sparse reconstruction from noisy undersampled measurements
when the following knowledge is available.(1) We are given partial, and partly erroneous …

This paper proposes a mixed low-rank approximation and second-order tensor-based total
variation (LRSOTTV) approach for the super-resolution and denoising of retinal optical …