RETRACTED ARTICLE: A Novel QACS Automatic Extraction Algorithm for Extracting Information in Blockchain-Based Systems

Zhang, Wenjing; Kaur, Mandeep

doi:10.1080/03772063.2022.2030252

Cited by 35 publications

(29 citation statements)

References 22 publications

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“…Let fg1, g2,⋯,gng be n frames of low-resolution images collected by existing hardware devices, and f is the high-resolution image to be reconstructed. As shown in Figure 2, the high-resolution image in Figure 2 Consistent with literature [11], the image degradation model is expressed as:…”

Section: Image Degradationmentioning

confidence: 71%

“…As shown in Figure 2 , the high-resolution image in Figure 2(a) is transformed into the result in Figure 2(b) after geometric transformation Tk, and then Figures 2(a) and 2(b) are, respectively, blurred (point spread function Hk and downsampling D); add noise to get Figures 2(c) and 2(d) . Consistent with literature [ 11 ], the image degradation model is expressed as:

…”

Section: Existing Frameworkmentioning

confidence: 99%

“…The most common ones are the nearest neighbor interpolation, bilinear interpolation, and bicubic interpolation. The existing literature proposes spatial nonlinear interpolation algorithms, wavelet-based algorithms, and bilinear interpolation-based methods [10][11][12] as interpolation-based image superresolution reconstruction method is easy to process, but due to the lack of sufficient prior knowledge and image observation model, the reconstructed image has blurred edges and poor overall visual effect.…”

Section: Biomed Research Internationalmentioning

confidence: 99%

“…Ignore this information. In the process of superresolution reconstruction, literature [11][12][13][14] all use Equation (6) as the image degradation model, directly discarding the subpixel information which will inevitably affect the accuracy of the reconstruction model. This paper tries to base on the degradation model ( 5) which establishes a super-resolution reconstruction model based on subpixel displacement to improve the accuracy of the reconstruction model.…”

Section: Image Degradationmentioning

confidence: 99%

“…A new system of polarization imaging technology has been evolved from the time-sharing and simultaneous polarization imaging technology [ 10 ]. Under the new imaging system, the system uses the Savart polarizer to modulate the four Stokes vectors of the detected target in the same interference image, so as to pass a single image as an input [ 11 ].…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Image Super-Resolution Reconstruction Method for Lung Cancer CT-Scanned Images Based on Neural Network

Liu

Qin

et al. 2022

BioMed Research International

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The super-resolution (SR) reconstruction of a single image is an important image synthesis task especially for medical applications. This paper is studying the application of image segmentation for lung cancer images. This research work is utilizing the power of deep learning for resolution reconstruction for lung cancer-based images. At present, the neural networks utilized for image segmentation and classification are suffering from the loss of information where information passes through one layer to another deep layer. The commonly used loss functions include content-based reconstruction loss and generative confrontation network. The sparse coding single-image super-resolution reconstruction algorithm can easily lead to the phenomenon of incorrect geometric structure in the reconstructed image. In order to solve the problem of excessive smoothness and blurring of the reconstructed image edges caused by the introduction of this self-similarity constraint, a two-layer reconstruction framework based on a smooth layer and a texture layer is proposed for a medical application of lung cancer. This method uses a global nonzero gradient number constrained reconstruction model to reconstruct the smooth layer. The proposed sparse coding method is used to reconstruct high-resolution texture images. Finally, a global and local optimization models are used to further improve the quality of the reconstructed image. An adaptive multiscale remote sensing image super-division reconstruction network is designed. The selective core network and adaptive gating unit are integrated to extract and fuse features to obtain a preliminary reconstruction. Through the proposed dual-drive module, the feature prior drive loss and task drive loss are transmitted to the super-resolution network. The proposed work not only improves the subjective visual effect but the robustness has also been enhanced with more accurate construction of edges. The statistical evaluators are used to test the viability of the proposed scheme.

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Section: Image Degradationmentioning

confidence: 71%

…”

Section: Existing Frameworkmentioning

confidence: 99%