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      Improved sparse domain super-resolution reconstruction algorithm based on CMUT

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          Abstract

          A novel breast ultrasound tomography system based on a circular array of capacitive micromechanical ultrasound transducers (CMUT) has broad application prospects. However, the images produced by this system are not suitable as input for the training phase of the super-resolution (SR) reconstruction algorithm. To solve the problem, this paper proposes an improved medical image super-resolution (MeSR) method based on the sparse domain. First, we use the simultaneous algebraic reconstruction technique (SART) with high imaging accuracy to reconstruct the image into a training image in a sparse domain model. Secondly, we denoise and enhance the contrast of the SART images to obtain improved detail images before training the dictionary. Then, we use the original detail image as the guide image to further process the improved detail image. Therefore, a high-precision dictionary was obtained during the testing phase and applied to filtered back projection SR reconstruction. We compared the proposed algorithm with previously reported algorithms in the Shepp Logan model and the model based on the CMUT background. The results showed significant improvements in peak signal-to-noise ratio, entropy, and average gradient compared to previously reported algorithms. The experimental results demonstrated that the proposed MeSR method can use noisy reconstructed images as input for the training phase of the SR algorithm and produce excellent visual effects.

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          Image super-resolution via sparse representation.

          This paper presents a new approach to single-image super-resolution, based on sparse signal representation. Research on image statistics suggests that image patches can be well-represented as a sparse linear combination of elements from an appropriately chosen over-complete dictionary. Inspired by this observation, we seek a sparse representation for each patch of the low-resolution input, and then use the coefficients of this representation to generate the high-resolution output. Theoretical results from compressed sensing suggest that under mild conditions, the sparse representation can be correctly recovered from the downsampled signals. By jointly training two dictionaries for the low- and high-resolution image patches, we can enforce the similarity of sparse representations between the low resolution and high resolution image patch pair with respect to their own dictionaries. Therefore, the sparse representation of a low resolution image patch can be applied with the high resolution image patch dictionary to generate a high resolution image patch. The learned dictionary pair is a more compact representation of the patch pairs, compared to previous approaches, which simply sample a large amount of image patch pairs, reducing the computational cost substantially. The effectiveness of such a sparsity prior is demonstrated for both general image super-resolution and the special case of face hallucination. In both cases, our algorithm generates high-resolution images that are competitive or even superior in quality to images produced by other similar SR methods. In addition, the local sparse modeling of our approach is naturally robust to noise, and therefore the proposed algorithm can handle super-resolution with noisy inputs in a more unified framework.
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            k-Wave: MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields.

            A new, freely available third party MATLAB toolbox for the simulation and reconstruction of photoacoustic wave fields is described. The toolbox, named k-Wave, is designed to make realistic photoacoustic modeling simple and fast. The forward simulations are based on a k-space pseudo-spectral time domain solution to coupled first-order acoustic equations for homogeneous or heterogeneous media in one, two, and three dimensions. The simulation functions can additionally be used as a flexible time reversal image reconstruction algorithm for an arbitrarily shaped measurement surface. A one-step image reconstruction algorithm for a planar detector geometry based on the fast Fourier transform (FFT) is also included. The architecture and use of the toolbox are described, and several novel modeling examples are given. First, the use of data interpolation is shown to considerably improve time reversal reconstructions when the measurement surface has only a sparse array of detector points. Second, by comparison with one-step, FFT-based reconstruction, time reversal is shown to be sufficiently general that it can also be used for finite-sized planar measurement surfaces. Last, the optimization of computational speed is demonstrated through parallel execution using a graphics processing unit.
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              Evaluation and development of deep neural networks for image super-resolution in optical microscopy

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                Author and article information

                Contributors
                Role: ConceptualizationRole: MethodologyRole: Writing – original draftRole: Writing – review & editing
                Role: SupervisionRole: Writing – review & editing
                Role: Data curationRole: Formal analysisRole: Supervision
                Role: Data curationRole: Funding acquisitionRole: SupervisionRole: Validation
                Role: ConceptualizationRole: Project administrationRole: ResourcesRole: SoftwareRole: Writing – review & editing
                Role: Funding acquisitionRole: Supervision
                Role: Funding acquisitionRole: Supervision
                Role: Editor
                Journal
                PLoS One
                PLoS One
                plos
                PLOS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                31 August 2023
                2023
                : 18
                : 8
                : e0290989
                Affiliations
                [1 ] School of Mathematics, North University of China, Taiyuan, China
                [2 ] Key Laboratory of Dynamic Testing Technology, School of Instrument and Electronics, North University of China, Taiyuan, China
                Hainan Normal University, CHINA
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                https://orcid.org/0000-0002-2043-8363
                Article
                PONE-D-23-12989
                10.1371/journal.pone.0290989
                10470967
                37651438
                afa3da59-83a8-4c30-8533-692ea8b30fdf
                © 2023 Wei et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 29 April 2023
                : 20 August 2023
                Page count
                Figures: 10, Tables: 4, Pages: 17
                Funding
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 61927807
                Award Recipient :
                Funded by: Fundamental Research Program of Shanxi Province
                Award ID: 202103021224195
                Award Recipient :
                Funded by: Fundamental Research Program of Shanxi Province
                Award ID: 202103021223189
                Award Recipient :
                Funded by: Fundamental Research Program of Shanxi Province
                Award ID: 202103021224212
                Award Recipient :
                Funded by: Fundamental Research Program of Shanxi Province
                Award ID: 20210302123019
                Award Recipient :
                Funded by: the 18th Graduate Science and Technology Project of Central North University
                Award ID: 20221848
                Award Recipient :
                Funded by: funder-id http://dx.doi.org/10.13039/501100001809, National Natural Science Foundation of China;
                Award ID: 61774137
                Award Recipient :
                This research is funded by the National Science Foundation of China as National Major Scientific Instruments Development Project, China (Grant No. 61927807). This research is funded by the Fundamental Research Program of Shanxi Province, China (Grant No. 202103021224195, 202103021223189, 202103021224212, 20210302123019), the National Science Foundation of China, China (Grant No. 61774137), the 18th Graduate Science and Technology Project of Central North University,China(Grant No.20221848). Financial support for this study came mainly from Professors Wendong Zhang, Guojun Zhang, Hongping Hu and Associate Professor Cheng Rong. Professors Wendong Zhang and Guojun Zhang were responsible for the revision of the manuscript and analysis of the experimental results, while Professors Hongping Hu and Associate Professor Cheng Rong are responsible for the data analysis, and conference expenses of researchers supported by these funding.
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