Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. Vannier, “Why do commercial CT scanners still employ traditional, filtered back-projection for image reconstruction?,” Inverse Problems, vol. The proposed networks may be readily applied to other image processing tasks including image denoising, image deblurring, and image super-resolution. It may not only suppress streak artifacts but also better preserve image details. The experimental results show that the RAD-UNet can improve the reconstruction accuracy compared with three existing representative deep networks. This network can improve the nonlinear fitting capability and the performance of suppressing streak artifacts. This network combines residual connection, attention mechanism, dense connection and perceptual loss. Through training via the large-scale training data, the RAD-UNet can obtain the capability of suppressing streak artifacts. Those images with streak artifacts are used as the input of the RAD-UNet, and the output-label images are the corresponding high-quality images. Then, the image is processed by the RAD-UNet to suppress streak artifacts and obtain high-quality CT image. The filtered back projection (FBP) algorithm is used to reconstruct the CT image with streak artifacts from sparse-view projections. To suppress the streak artifacts in images reconstructed from sparse-view projections in computed tomography (CT), a residual, attention-based, dense UNet (RAD-UNet) deep network is proposed to achieve accurate sparse reconstruction.
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