Calculation of Projection Matrix in Image Reconstruction Based on Neural Network

Lei, Bingzhen; Xiu-qing, LI; Zhang, Jun; Wen, Junhai

doi:10.1109/iccia.2018.00038

Cited by 2 publications

(1 citation statement)

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“…For example, a large number of iterations and a long execution time are involved in the IR algorithms. To overcome the shortcomings of the traditional IR methods while ensuring high performance, a neural network was introduced for the IR [5]. Inspired by the outstanding performance of a convolutional neural network (CNN) in extracting signal features, Xiao et al proposed an excellent CNN approach for brightness temperature IR mirrored aperture synthesis (MAS) [6], where a deep CNN for learning the MAS IR mapping and system errors was employed to improve the brightness temperature IR.…”

Section: Introductionmentioning

confidence: 99%

Image Reconstruction with Multiscale Interest Points Based on a Conditional Generative Adversarial Network

et al. 2022

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A new image reconstruction (IR) algorithm from multiscale interest points in the discrete wavelet transform (DWT) domain was proposed based on a modified conditional generative adversarial network (CGAN). The proposed IR-DWT-CGAN model generally integrated a DWT module, an interest point extraction module, an inverse DWT module, and a CGAN. First, the image was transformed using the DWT to provide multi-resolution wavelet analysis. Then, the multiscale maxima points were treated as interest points and extracted in the DWT domain. The generator was a U-net structure to reconstruct the original image from a very coarse version of the image obtained from the inverse DWT of the interest points. The discriminator network was a fully convolutional network, which was used to distinguish the restored image from the real one. The experimental results on three public datasets showed that the proposed IR-DWT-CGAN model had an average increase of 2.9% in the mean structural similarity, an average decrease of 39.6% in the relative dimensionless global error in synthesis, and an average decrease of 48% in the root-mean-square error compared with several other state-of-the-art methods. Therefore, the proposed IR-DWT-CGAN model is feasible and effective for image reconstruction with multiscale interest points.

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Section: Introductionmentioning

confidence: 99%