inverse problem

The Importance of Phase in Complex Compressive Sensing

Abstract: We consider the question of estimating a real low-complexity signal (such as a sparse vector or a low-rank matrix) from the phase of complex random measurements. We show that in this phase-only compressive sensing (PO-CS) scenario, we can perfectly recover such a signal with high probability and up to global unknown amplitude if the sensing matrix is a complex Gaussian random matrix and the number of measurements is large compared to the complexity level of the signal space.

Compressive Sampling Approach for Image Acquisition with Lensless Endoscope

Abstract: The lensless endoscope is a promising device designed to image tissues in vivo at the cellular scale. The traditional acquisition setup consists in raster scanning during which the focused light beam from the optical fiber illuminates sequentially each pixel of the field of view (FOV).

Blind Deconvolution of PET Images using Anatomical Priors

Abstract: Images from positron emission tomography (PET) provide metabolic information about the human body. They present, however, a spatial resolution that is limited by physical and instrumental factors often modeled by a blurring function.

Compressive Hyperspectral Imaging with Fourier Transform Interferometry

Abstract: This paper studies the fast acquisition of Hyper- Spectral (HS) data using Fourier transform interferometry (FTI). FTI has emerged as a promising alternative to capture, at a very high resolution, the wavelength coordinate as well as the spatial domain of the HS volume.

Image Deconvolution by Local Order Preservation of Pixels Values

Abstract: Positron emission tomography is more and more used in radiation oncology, since it conveys useful functional information about cancerous lesions. Its rather low spatial resolution, however, prevents accurate tumor delineation and heterogeneity assessment.

Low Rank and Group-Average Sparsity Driven Convex Optimization for Direct Exoplanets Imaging

Abstract: Direct exoplanets imaging is a challenging task for two main reasons. First, the host star is several order of magnitude brighter than exoplanets. Second, the great distance between us and the star system makes the exoplanets-star angular dis- tance very small.

A modified 4D ROOSTER method using the Chambolle-Pock algorithm

Abstract: The 4D RecOnstructiOn using Spatial and TEmpo- ral Regularization method is a recent 4D cone beam computed tomography algorithm. 4D ROOSTER has not been rigorously proved to converge. This paper aims to reformulate it using the Chambolle & Pock primal-dual optimization scheme.

Bits of Images: Inverting Local Image Binary Descriptors

(print (nth 5 (org-heading-components))) Abstract: Local Binary Descriptors (LBDs) are good at matching image parts, but what information is actually carried? This question is usually masked by a comparison of matching performances.