Performance Of Image Reconstruction Algorithms Research Articles

Compressed imaging with focused light

Compressed sensing applied to optical microscopy enables imaging with a number of measurements below the Nyquist criterion. The illumination basis selected, often unstructured for randomness considerations, influences the performance of image reconstruction algorithms. Here, we show through modelling based on multimode fiber imaging that an illumination basis composed of a series of uniformly spaced foci provides improved robustness to noise, increased volumetric imaging performance, and greater resilience to external perturbation when compared to a speckle illumination basis. These observations have broad implications for computational super-resolution imaging, endo-microscopy, and post-processing of images acquired with any point-scanning imaging system.

Block Coordinate Regularization by Denoising

We consider the problem of reconstructing an image from its noisy measurements using a prior specified only with an image denoiser. Recent work on plug-and-play priors (PnP) and regularization by denoising (RED) has shown the state-of-the-art performance of image reconstruction algorithms under such priors in a range of imaging problems. In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables. We theoretically analyze the convergence of the algorithm and discuss its relationship to the traditional proximal optimization. Our analysis complements and extends recent theoretical results for RED-based estimation methods. We numerically validate our method using several denoising priors, including those based on deep neural nets.

Evaluation of Image Reconstruction Algorithms for Confocal Microwave Imaging: Application to Patient Data.

Confocal Microwave Imaging (CMI) for the early detection of breast cancer has been under development for over two decades and is currently going through early-phase clinical evaluation. The image reconstruction algorithm is a key signal processing component of any CMI-based breast imaging system and impacts the efficacy of CMI in detecting breast cancer. Several image reconstruction algorithms for CMI have been developed since its inception. These image reconstruction algorithms have been previously evaluated and compared, using both numerical and physical breast models, and healthy volunteer data. However, no study has been performed to evaluate the performance of image reconstruction algorithms using clinical patient data. In this study, a variety of imaging algorithms, including both data-independent and data-adaptive algorithms, were evaluated using data obtained from a small-scale patient study conducted at the University of Calgary. Six imaging algorithms were applied to reconstruct 3D images of five clinical patients. Reconstructed images for each algorithm and each patient were compared to the available clinical reports, in terms of abnormality detection and localisation. The imaging quality of each algorithm was evaluated using appropriate quality metrics. The results of the conventional Delay-and-Sum algorithm and the Delay-Multiply-and-Sum (DMAS) algorithm were found to be consistent with the clinical information, with DMAS producing better quality images compared to all other algorithms.

Hybrid MPI-OpenMP Parallelization of Image Reconstruction

Perceptual coding should take full advantage of the results from human visual system (HVS) studies. In this paper, we exploit an image reconstruction algorithm, which can simply and reliably represent images using regional shape and texture information and effectually eliminate perceptual redundancy. But real-time application of the algorithm is limited due to its inherent complex and intensive computation. Therefore, we implement image reconstruction with a hybrid message passing interface (MPI) and OpenMP approach on the basis of a standard MPI implementation. Experimental results confirm our image reconstruction algorithm can get optimal restoration results. Compared with JPEG and JPEG2000 compression, this algorithm can not only get more coding gain but remain most information people care. Our hybrid MPI-OpenMP approach can significantly improve time performance of image reconstruction algorithm.

Hybrid coded aperture and Compton imaging using an active mask

The trimodal imager (TMI) images gamma-ray sources from a mobile platform using both coded aperture (CA) and Compton imaging (CI) modalities. In this paper we will discuss development and performance of image reconstruction algorithms for the TMI. In order to develop algorithms in parallel with detector hardware we are using a GEANT4 [J. Allison, K. Amako, J. Apostolakis, H. Araujo, P.A. Dubois, M. Asai, G. Barrand, R. Capra, S. Chauvie, R. Chytracek, G. Cirrone, G. Cooperman, G. Cosmo, G. Cuttone, G. Daquino, et al., IEEE Trans. Nucl. Sci. NS-53 (1) (2006) 270] based simulation package to produce realistic data sets for code development. The simulation code incorporates detailed detector modeling, contributions from natural background radiation, and validation of simulation results against measured data. Maximum likelihood algorithms for both imaging methods are discussed, as well as a hybrid imaging algorithm wherein CA and CI information is fused to generate a higher fidelity reconstruction.