scholarly journals Generalized Image Reconstruction in Optical Coherence Tomography Using Redundant and Non-Uniformly-Spaced Samples

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7057
Author(s):  
Karim Nagib ◽  
Biniyam Mezgebo ◽  
Namal Fernando ◽  
Behzad Kordi ◽  
Sherif S. Sherif
Keyword(s):  
Fourier Transform ◽  
Image Reconstruction ◽  
Frequency Domain ◽  
Signal To Noise Ratio ◽  
Frame Theory ◽  
Reconstruction Method ◽  
Signal To Noise ◽  
Image Reconstruction Method ◽  
Special Cases ◽  
Oct Image

In this paper, we use Frame Theory to develop a generalized OCT image reconstruction method using redundant and non-uniformly spaced frequency domain samples that includes using non-redundant and uniformly spaced samples as special cases. We also correct an important theoretical error in the previously reported results related to OCT image reconstruction using the Non-uniform Discrete Fourier Transform (NDFT). Moreover, we describe an efficient method to compute our corrected reconstruction transform, i.e., a scaled NDFT, using the Fast Fourier Transform (FFT). Finally, we demonstrate different advantages of our generalized OCT image reconstruction method by achieving (1) theoretically corrected OCT image reconstruction directly from non-uniformly spaced frequency domain samples; (2) a novel OCT image reconstruction method with a higher signal-to-noise ratio (SNR) using redundant frequency domain samples. Our new image reconstruction method is an improvement of OCT technology, so it could benefit all OCT applications.

Image Compression and Reconstruction based on Fuzzy Relation and Soft Computing Technology

2004 ◽  
Vol 8 (1) ◽  
pp. 72-80
Author(s):  
Kaoru Hirota ◽  
◽  
Hajime Nobuhara ◽  
Kazuhiko Kawamoto ◽  
Shin’ichi Yoshida
Keyword(s):  
Image Reconstruction ◽  
Image Compression ◽  
Soft Computing ◽  
Signal To Noise Ratio ◽  
Color Space ◽  
Image Data ◽  
Compression Rate ◽  
Reconstruction Method ◽  
Image Reconstruction Method ◽  
Fuzzy Relational Equation

A fast image reconstruction method for Image Compression method based on Fuzzy relational equation (ICF) and soft computing is proposed. In experiments using 20 images (Standard Image DataBAse), the decrease in image reconstruction time to 1/132.02 and 1/382.29 are obtained when the compression rate is 0.0156 and 0.0625, respectively, and the proposed method outperforms the conventional one in the Peak Signal to Noise Ratio (PSNR). ICF using nonuniform coders over YUV color space is proposed in order to achieve effective compression. Linear quantization of compressed image data is introduced in order to improve the compression rate. Through experiments using 100 typical images (Corel Gallery, Arizona Directory), PSNR increases at 7.9-14.1% compared with the conventional method under the condition that compression rates are 0.0234-0.0938.

Image reconstruction method based on CCD calibration in frequency domain

2015 ◽  
Vol 54 (14) ◽  
pp. 4561
Author(s):  
Sheng-Jun Xiong ◽  
Bin Xiangli ◽  
Yang He ◽  
Ze Zhang
Keyword(s):  
Image Reconstruction ◽  
Frequency Domain ◽  
Reconstruction Method ◽  
Image Reconstruction Method

scholarly journals A New Method for Superresolution Image Reconstruction Based on Surveying Adjustment

2014 ◽  
Vol 2014 ◽  
pp. 1-6 ◽  
Author(s):  
Jianjun Zhu ◽  
Cui Zhou ◽  
Donghao Fan ◽  
Jinghong Zhou
Keyword(s):  
Image Reconstruction ◽  
Convex Sets ◽  
Signal To Noise Ratio ◽  
Main Idea ◽  
New Method ◽  
Projection Algorithm ◽  
Reconstruction Method ◽  
Reconstruction Process ◽  
Adjustment Method ◽  
Image Reconstruction Method

A new method for superresolution image reconstruction based on surveying adjustment method is described in this paper. The main idea of such new method is that a sequence of low-resolution images are taken firstly as observations, and then observation equations are established for the superresolution image reconstruction. The gray function of the object surface can be found by using surveying adjustment method from the observation equations. High-resolution pixel value of the corresponding area can be calculated by using the gray function. The results show that the proposed algorithm converges much faster than that of conventional superresolution image reconstruction method. By using the new method, the visual feeling of reconstructed image can be greatly improved compared to that of iterative back projection algorithm, and its peak signal-to-noise ratio can also be improved by nearly 1 dB higher than the projection onto convex sets algorithm. Furthermore, this method can successfully avoid the ill-posed problems in reconstruction process.

scholarly journals High-Resolution Ultrasound Imaging Enabled by Random Interference and Joint Image Reconstruction

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6434
Author(s):  
Pavel Ni ◽  
Heung-No Lee
Keyword(s):  
High Resolution ◽  
Image Reconstruction ◽  
Ultrasound Imaging ◽  
Signal To Noise Ratio ◽  
Reconstruction Method ◽  
Signal To Noise ◽  
Joint Reconstruction ◽  
Reconstruction Scheme ◽  
Reconstruction Methods ◽  
Noise Ratio

In ultrasound, wave interference is an undesirable effect that degrades the resolution of the images. We have recently shown that a wavefront of random interference can be used to reconstruct high-resolution ultrasound images. In this study, we further improve the resolution of interference-based ultrasound imaging by proposing a joint image reconstruction scheme. The proposed reconstruction scheme utilizes radio frequency (RF) signals from all elements of the sensor array in a joint optimization problem to directly reconstruct the final high-resolution image. By jointly processing array signals, we significantly improved the resolution of interference-based imaging. We compare the proposed joint reconstruction method with popular beamforming techniques and the previously proposed interference-based compound method. The simulation study suggests that, among the different reconstruction methods, the joint reconstruction method has the lowest mean-squared error (MSE), the best peak signal-to-noise ratio (PSNR), and the best signal-to-noise ratio (SNR). Similarly, the joint reconstruction method has an exceptional structural similarity index (SSIM) of 0.998. Experimental studies showed that the quality of images significantly improved when compared to other image reconstruction methods. Furthermore, we share our simulation codes as an open-source repository in support of reproducible research.

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.

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