scholarly journals Statistical Prior Aided Separate Compressed Image Sensing for Green Internet of Multimedia Things

2017 ◽  
Vol 2017 ◽  
pp. 1-12
Author(s):  
Shaohua Wu ◽  
Tiantian Zhang ◽  
Jian Jiao ◽  
Jingran Yang ◽  
Qinyu Zhang
Keyword(s):  
Image Reconstruction ◽  
Weight Matrix ◽  
Reconstruction Method ◽  
Wavelet Domain ◽  
Image Sensing ◽  
Fixed Weight ◽  
Exponential Decay Model ◽  
Image Representations ◽  
Core Idea ◽  
Statistical Prior

In this paper, we aim to propose an image compression and reconstruction strategy under the compressed sensing (CS) framework to enable the green computation and communication for the Internet of Multimedia Things (IoMT). The core idea is to explore the statistics of image representations in the wavelet domain to aid the reconstruction method design. Specifically, the energy distribution of natural images in the wavelet domain is well characterized by an exponential decay model and then used in the two-step separate image reconstruction method, by which the row-wise (or column-wise) intermediates and column-wise (or row-wise) final results are reconstructed sequentially. Both the intermediates and the final results are constrained to conform with the statistical prior by using a weight matrix. Two recovery strategies with different levels of complexity, namely, the direct recovery with fixed weight matrix (DR-FM) and the iterative recovery with refined weight matrix (IR-RM), are designed to obtain different quality of recovery. Extensive simulations show that both DR-FM and IR-RM can achieve much better image reconstruction quality with much faster recovery speed than traditional methods.

Wavelet-Domain High Resolution Image Reconstruction

2013 ◽  
Vol 756-759 ◽  
pp. 3309-3312
Author(s):  
Li Wen Dong
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Reconstruction Method ◽  
Wavelet Domain ◽  
Reconstruction Process ◽  
Resolution Image ◽  
High Resolution Image ◽  
Image Reconstruction Method ◽  
High Resolution Images

The aliasing due to subsampling and the blur from the finite detector size can decrease quality of the images and make fine details and structures difficult to interpret. High resolution images can be reconstructed from several adjacent frames in a sequence by reconstruction process. This paper describes the high resolution image reconstruction method based on wavelet domain. In this approach both the image sequences and the degradation operator are presented by orthogonal wavelet with compact support. Experimental results demonstrate that the proposed method is effective to improve image details.

Image Reconstruction with the Fourier Coefficients for Magnetic Induction Tomography

2020 ◽  
Vol 16 (2) ◽  
pp. 156-163
Author(s):  
Jingwen Wang ◽  
Xu Wang ◽  
Dan Yang ◽  
Kaiyang Wang
Keyword(s):  
Inverse Problem ◽  
Image Reconstruction ◽  
Magnetic Induction ◽  
Fourier Coefficients ◽  
Signal Reconstruction ◽  
Reconstruction Algorithm ◽  
Reconstruction Method ◽  
Measurement Equation ◽  
Image Reconstruction Method ◽  
Magnetic Induction Tomography

Background: Image reconstruction of magnetic induction tomography (MIT) is a typical ill-posed inverse problem, which means that the measurements are always far from enough. Thus, MIT image reconstruction results using conventional algorithms such as linear back projection and Landweber often suffer from limitations such as low resolution and blurred edges. Methods: In this paper, based on the recent finite rate of innovation (FRI) framework, a novel image reconstruction method with MIT system is presented. Results: This is achieved through modeling and sampling the MIT signals in FRI framework, resulting in a few new measurements, namely, fourier coefficients. Because each new measurement contains all the pixel position and conductivity information of the dense phase medium, the illposed inverse problem can be improved, by rebuilding the MIT measurement equation with the measurement voltage and the new measurements. Finally, a sparsity-based signal reconstruction algorithm is presented to reconstruct the original MIT image signal, by solving this new measurement equation. Conclusion: Experiments show that the proposed method has better indicators such as image error and correlation coefficient. Therefore, it is a kind of MIT image reconstruction method with high accuracy.

scholarly journals Effect of PET-MR Inconsistency in the Kernel Image Reconstruction Method

2019 ◽  
Vol 3 (4) ◽  
pp. 400-409 ◽  
Author(s):  
Daniel Deidda ◽  
N. A. Karakatsanis ◽  
Philip M. Robson ◽  
Nikos Efthimiou ◽  
Zahi A. Fayad ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Reconstruction Method ◽  
Image Reconstruction Method
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