The process of single-image super-resolution (SR) has certain limitations, such as an insufficient utilization of high-frequency information in images and a network structure that is insufficiently flexible to reconstruct the feature information of different complexities. Therefore, deep iterative residual back-projection networks are proposed. Residual learning was used to ease the difficulty in training and fully discover the feature information of the image, and a back-projection method was applied to study the interdependence between high- and low-resolution images. In addition, the network structure reconstructs smooth-feature and high-frequency information of the image separately and transmits only the residual features among all residual blocks of the network structure. The experiment results show that compared with most single-frame image SR methods, the proposed approach not only achieves a significant improvement in objective indicators, but it also provides richer texture information in the reconstructed predicted image.
KEYWORDS: Super resolution, 3D magnetic resonance imaging, Magnetic resonance imaging, Lawrencium, Image resolution, Image processing, 3D image processing, Associative arrays, Image restoration, Medical imaging
Clinical practice requires multiple scans with different modalities for diagnostic tasks, but each scan does not produce the image of the same resolution. Such phenomenon may influence the subsequent analysis such as registration or multimodal segmentation. Therefore, performing super-resolution (SR) on clinical images is needed. In this paper, we present a unified SR framework which takes advantages of two primary SR approaches – self-learning SR and learning-based SR. Through the self-learning SR process, we succeed in obtaining a second-order approximation of the mapping functions between low and high resolution image patches, by leveraging a local regression model and multi-scale self-similarity. Through the learning-based SR process, such patch relations are further refined by using the information from a reference HR image. Extensive experiments on open-access MRI images have validated the effectiveness of the proposed method. Compared to other advanced SR approaches, the proposed method provides more realistic HR images with sharp edges.
The challenge of learning-based superresolution (SR) is to predict the relationships between low-resolution (LR) patches and their corresponding high-resolution (HR) patches. By learning such relationships from external training images, the existing learning-based SR approaches are often affected by the relevance between the training data and the LR input image. Therefore, we propose a single-image SR method that learns the LR-HR relations from the given LR image itself instead of any external images. Both the local regression model and nonlocal patch redundancy are exploited in the proposed method. The local regression model is employed to derive the mapping functions between self-LR-HR example patches, and the nonlocal self-similarity gives rise to a high-order derivative estimation of the derived mapping function. Moreover, to fully exploit the multiscale similarities inside the LR input image, we accumulate the previous reconstruction results and their corresponding LR versions as additional example patches for the subsequent estimation process, and adopt a gradual magnification scheme to achieve the desired zooming size step by step. Extensive experiments on benchmark images have validated the effectiveness of the proposed method. Compared to other state-of-the-art SR approaches, the proposed method provides photorealistic HR images with sharp edges.
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