Image super-resolution (SR) is one of the vital image processing methods that improve the resolution of an image in the field of computer vision. In the last two decades, significant progress has been made in the field of super-resolution, especially by utilizing deep learning methods. This survey is an effort to provide a detailed survey of recent progress in single-image super-resolution in the perspective of deep learning while also informing about the initial classical methods used for image super-resolution. The survey classifies the image SR methods into four categories, i.e., classical methods, supervised learning-based methods, unsupervised learning-based methods, and domain-specific SR methods. We also introduce the problem of SR to provide intuition about image quality metrics, available reference datasets, and SR challenges. Deep learning-based approaches of SR are evaluated using a reference dataset. Some of the reviewed state-of-the-art image SR methods include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN), multiscale residual network (MSRN), meta residual dense network (Meta-RDN), recurrent back-projection network (RBPN), second-order attention network (SAN), SR feedback network (SRFBN) and the wavelet-based residual attention network (WRAN). Finally, this survey is concluded with future directions and trends in SR and open problems in SR to be addressed by the researchers.

Diffusion Tensor Magnetic Resonance Imaging (DT-MRI, also known as DTI) measures the diffusion properties of water molecules in tissues and to date is one of the main techniques that can effectively study the microstructures of the brain in vivo. Presently, evaluation of DTI registration techniques is still in an initial stage of development. In this paper, six well-known open source DTI registration algorithms: Elastic, Rigid, Affine, DTI-TK, FSL and SyN were applied on 11 subjects from an open-access dataset, among which one was randomly chosen as the template. Eight different fiber bundles of 10 subjects and the template were obtained by drawing regions of interest (ROIs) around various structures using deterministic streamline tractography. The performances of the registration algorithms were evaluated by computing the distances and intersection angles between fiber tracts, as well as the fractional anisotropy (FA) profiles along the fiber tracts. Also, the mean squared error (MSE) and the residual MSE (RMSE) of fibers originating from the registered subjects and the template were calculated to assess the registration algorithm. Twenty-seven different fiber bundles of the 10 subjects and template were obtained by drawing ROIs around various structures using probabilistic tractography. The performances of registration algorithms on this second tractography method were evaluated by computing the spatial correlation similarity of the fibers between subjects as well as between each subject and the template. All experimental results indicated that DTI-TK performed the best under the study conditions, and SyN ranked just behind it.

There are relatively few studies on the multicoil reconstruction task of existing Magnetic resonance imaging (MRI) methods, as there are problems with insufficient reconstruction details, high memory occupation during training, etc. Therefore, a new Dual domain Parallel Fusion Reconstruction Network (DPFNet) is proposed in this paper. The whole network consists of coil sensitivity graph estimation module, dual domain feature extraction module, dual domain dynamic error correction module, and dual domain dynamic fusion module. A U-Net has been used as the backbone network. The network reconstructs undersampled MRI images and K-space data simultaneously in two branches of the image domain and K-space domain, and the fusion module realizes the reconstruction information interaction between the two branches. In addition, a new dual domain consistency loss is also proposed, which reduces the error between the same MRI slice image and K-space data with dual domain output, and achieves high quality reconstruction. In this paper, a series of comparative experiments and ablation experiments are conducted in the open Calgary-Campinas-359 brain MRI data set. The results of the experiments show that the proposed DPFNet achieves the most advanced level at present and is superior to other traditional algorithms and reconstruction methods based on deep learning. In particular, the reconstruction results from Cartesian sampling are very good

Diffusion Tensor Magnetic Resonance Imaging (DTI) is a non-invasive technique for studying brain structure in vivo by measuring the diffusion properties of water molecules. Unlike conventional medical imaging that captures scalar intensity data, DTI data is typically stored as a 4D volume, where each voxel in 3D space is a 3×3 Cartesian tensor. DTI characterizes tensor-based diffusion profiles and captures information about the orientation of fiber bundles. During the alignment process, voxels need to be spatially transformed while maintaining the correspondence of tensor orientations, which leads to complex computations. Traditional DTI registration methods often suffer from slow iteration speed and low accuracy, posing challenges for clinical applications. In this paper, a novel DTI Registration method Based on Parallel Dual-channel Voxel Morph (DTI-RBPDV) is proposed. The core of the method is a two-branch convolutional neural network architecture. With a view to enhancing the alignment performance, it processes two input patterns simultaneously: (1) fractional anisotropy (FA) images and (2) principal eigenvectors from to-be-aligned and fixed DTI volumes to enhance the accuracy of deformation field prediction. In the network decoder layer, integration of attention mechanisms has also been implemented. These channel space attention modules dynamically highlight salient anatomical features and orientation consistency, improving the model's sensitivity to key structural alignments. Experimental results show that DTI-RBPDV effectively addresses the limitations of slow iterative computation and the challenges of applying deep learning to high-dimensional DTI data by significantly improving the registration accuracy and computational speed.