Under consideration is the large body of signal recovery problems that can be formulated as the problem of minimizing the sum of two (not necessarily smooth) proper lower semicontinuous convex functions in a real Hilbert space. This generic problem is analyzed and a decomposition method is proposed to solve it. The convergence of the method, which is based on an extension of the Douglas-Rachford algorithm for monotone operators splitting, is established under general conditions. Various signal recovery applications are discussed and numerical results are provided

Improvements in electromagnetic sources, detectors, optical components, and computational imaging have made it possible to achieve three-dimensional atomic-scale resolution using tomographic phase-contrast imaging techniques. These greater capabilities have placed a premium on improving the efficiency and stability of phase retrieval algorithms for recovering the missing phase information in diffraction observations. In some cases, so called direct methods suffice, but, for large macromolecules and nonperiodic structures, one must rely on numerical techniques for reconstructing the missing phase. This is the principal motivation of our work. We report on recent progress in algorithms for iterative phase retrieval. The theory of convex optimisation is used to develop and to gain insight into counterparts for the nonconvex problem of phase retrieval. We propose a relaxation of averaged alternating reflectors and determine the fundamental mathematical properties of the related operator in the convex case. Numerical studies support our theoretical observations and demonstrate the effectiveness of the newer generation of algorithms compared to the current state of the art.

In this paper, we report a novel heuristic for requantizing JPEG images. The resulting images are generally smaller and often have improved perceptual image quality over a "blind" requantization approach, that is, one that does not consider the properties of the quantization matrices. The heuristic is supported by a detailed mathematical treatment which incorporates the well-known Laplacian distribution of the AC discrete cosine transform (DCT) coefficients with an analysis of the error introduced by requantization. We note that the technique is applicable to any image compression method which employs discrete cosine transforms and quantization.

We propose a new method for requantizing JPEG images, based on the well-known Laplacian distribution of the AC discrete cosine transform (DCT) coefficients and on an analysis of the error introduced by requantization. The resulting images have improved perceptual image quality over a “blind” requantization approach, that is, one that does not consider the properties of the quantization matrices.

The state of the art for solving the phase retrieval problem in two dimensions relies heavily on the algorithms proposed by Gerchbercy, Saxton, and Fienup. Despite the widespread use of these algorithms, current mathematical theory cannot explain their remarkable success. It is already known that the Gerchberg-Saxton algorithm is a nonconvex version of method of alternating projections. In this paper, we show that two other prominent phase retrieval methods also have well known counterparts in the world of convex optimization algorithms: Fienup's basic input-output algorithm corresponds to Dykstra's algorithm, and Fienup's hybrid input-output algorithm can be viewed as an instance of the Douglas-Rachford algorithm. This work provides a theoretical framework to better understand and, potentially, improve existing phase recovery algorithms.

In this paper we present two variants of the EM algorithm for dynamic SPECT imaging. A version based on compartmental modeling which fits a sum of exponentials and a more general approach allowing for arbitrary decaying activities. The underlying probabilistic models are discussed and the incomplete and complete data spaces are shown to be physically meaningful. We indicate that the second method, leading to a convex program in the M step, is easier to treat numerically and we present a possible numerical approach. Some preliminary numerical tests indicating the feasibility of the method are included.