Article

Digital Image Reconstruction: Deblurring and Denoising

Los Alamos National Laboratory, Лос-Аламос, California, United States
Annual Review of Astronomy and Astrophysics (Impact Factor: 24.04). 08/2005; 43(1):139-194. DOI: 10.1146/annurev.astro.43.112904.104850

ABSTRACT ▪ Abstract Digital image reconstruction is a robust means by which the underlying images hidden in blurry and noisy data can be revealed. The main challenge is sensitivity to measurement noise in the input data, which can be magnified strongly, resulting in large artifacts in the reconstructed image. The cure is to restrict the permitted images. This review summarizes image reconstruction methods in current use. Progressively more sophisticated image restrictions have been developed, including (a) filtering the input data, (b) regularization by global penalty functions, and (c) spatially adaptive methods that impose a variable degree of restriction across the image. The most reliable reconstruction is the most conservative one, which seeks the simplest underlying image consistent with the input data. Simplicity is context-dependent, but for most imaging applications, the simplest reconstructed image is the smoothest one. Imposing the maximum, spatially adaptive smoothing permitted by the data results in t...

0 Followers
 · 
110 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We investigate the problem of reconstructing signals and images from a subsampled convolution of masked snapshots and a known filter. The problem is studied in the context of coded imaging systems, where the diversity provided by the random masks makes the deconvolution problem significantly better conditioned than it would be from a set of direct measurements. We start by studying the conditioning of the forward linear measurement operator that describes the system in terms of the number of masks $K$, the dimension of image $L$, the number of sensors $N$, and certain characteristics of the blur kernel. We show that stable deconvolution is possible when $KN \geq L\log L$, meaning that the total number of sensor measurements is within a logarithmic factor of the image size. Next, we consider the scenario where the target image is known to be sparse. We show that under mild conditions on the blurring kernel, the linear system is a restricted isometry when the number of masks is within a logarithmic factor of the number of active components, making the image recoverable using any one of a number of sparse recovery techniques.
  • [Show abstract] [Hide abstract]
    ABSTRACT: In this article, for the reconstruction of the positron emission tomography (PET) images, an iterative MAP algorithm was instigated with its adaptive neurofuzzy inference system based image segmentation techniques which we call adaptive neurofuzzy inference system based expectation maximization algorithm (ANFIS-EM). This expectation maximization (EM) algorithm provides better image quality when compared with other traditional methodologies. The efficient result can be obtained using ANFIS-EM algorithm. Unlike any usual EM algorithm, the predicted method that we call ANFIS-EM minimizes the EM objective function using maximum a posteriori (MAP) method. In proposed method, the ANFIS-EM algorithm was instigated by neural network based segmentation process in the image reconstruction. By the image quality parameter of PSNR value, the adaptive neurofuzzy based MAP algorithm and de-noising algorithm compared and the PET input image is reconstructed and simulated in MATLAB/simulink package. Thus ANFIS-EM algorithm provides 40% better peak signal to noise ratio (PSNR) when compared with MAP algorithm. © 2015 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 25, 1–6, 2015
    International Journal of Imaging Systems and Technology 03/2015; 25(1). DOI:10.1002/ima.22114 · 0.77 Impact Factor
  • Source

Full-text (2 Sources)

Download
56 Downloads
Available from
Jun 2, 2014