Super Resolution of MRI Using Improved IBP

Acquiring High Resolution (HR) Magnetic Resonance (MR) images requires the patient to remain still for long periods of time, which causes patient discomfort and increases the probability of motion induced image artifacts. A possible solution is to acquire low resolution (LR) images and to process them with the Super Resolution Generative Adversarial Network (SRGAN) to create a super-resolved version. This work applies SRGAN to MR images of the prostate and performs three experiments. The first experiment explores improving the in-plane MR image resolution by factors of 4 and 8, and shows that, while the PSNR and SSIM (Structural SIMilarity) metrics are lower than the isotropic bicubic interpolation baseline, the SRGAN is able to create images that have high edge fidelity. The second experiment explores anisotropic super-resolution via synthetic images, in that the input images to the network are anisotropically downsampled versions of HR images. This experiment demonstrates the ability of the modified SRGAN to perform anisotropic super-resolution, with quantitative image metrics that are comparable to those of the anisotropic bicubic interpolation baseline. Finally, the third experiment applies a modified version of the SRGAN to super-resolve anisotropic images obtained from the through-plane slices of the volumetric MR data. The output super-resolved images contain a significant amount of high frequency information that make them visually close to their HR counterparts. Overall, the promising results from each experiment show that super-resolution for MR images is a successful technique and that producing isotropic MR image volumes from anisotropic slices is an achievable goal.

Security is one of the most important things in our daily lives and the security business is a big industry. Security cameras are set in many areas in order to keep us safe. When a crime occurs, we often hear that police analyze the security camera footage. It takes long time and it is often the case that the footage is not helpful in the investigation at all. The reason is very simple. Many of the security camera images do not have sufficient resolution. Currently there are many super resolution (SR) technologies. In detective dramas technicians solve crimes using SR technologies turning blurry images taken with a security camera into sharp high resolution ones with a click of a button. However, in real life it is not so easy. In this paper we discuss the current issues and limitations of the SR technologies.

Super-resolution (SR) aims at generating a high resolution image or image sequence from several slightly different low-resolution images. In previous work, we have showed that a Maximum A Posteriori (MAP) SR algorithm can yield significant image quality enhancement of the individual respiratory gated PET frames. However, in the past it has been also argued that image-based motion correction algorithms yield sub-optimal results, particularly when images are reconstructed using iterative methods. Here, we evaluate the results incorporating MAP SR directly as part of the One-Step-Late (OSL) reconstruction algorithm. The evaluation used GATE simulated data. The IEC phantom with lesions between 10–37mm (contrast 8/1) was used to simulate 200 noise realizations of 6 frames, each shifted by 3mm along the z-axis relative to each other. The algorithm was also tested on four realistic phantoms. Gated respiratory images were reconstructed with the OPL-EM algorithm. Superresolution was performed through a MAP algorithm either as part of the reconstruction process or as a post-processing step applied to the individually reconstructed gated frames. A Huber prior was used as regularization term while the function yielded by the MAP method was optimized through a quasi-Newton algorithm. We compared signal-to-noise ratio (SNR) and contrast, as well as the bias-variance tradeoff for each algorithm. SR incorporated reconstruction led to 9% and 14% higher SNR and contrast respectively compared to the image-based SR. The bias-variance tradeoff showed that reconstruction-incorporated SR has a bias and variance of 39% lower and 60% higher respectively compared to image-based SR.

Deep learning superresolution (SR) is a promising approach to reduce MRI scan time without requiring custom sequences or iterative reconstruction. Previous deep learning SR approaches have generated low-resolution training images by simple k-space truncation, but this does not properly model in-plane turbo spin echo (TSE) MRI resolution degradation, which has variable T2 relaxation effects in different k-space regions. To fill this gap, we developed a T2 -deblurred deep learning SR method for the SR of 3D-TSE images. A SR generative adversarial network was trained using physically realistic resolution degradation (asymmetric T2 weighting of raw high-resolution k-space data). For comparison, we trained the same network structure on previous degradation models without TSE physics modeling. We tested all models for both retrospective and prospective SR with 3 × 3 acceleration factor (in the two phase-encoding directions) of genetically engineered mouse embryo model TSE-MR images. The proposed method can produce high-quality 3 × 3 SR images for a typical 500-slice volume with 6-7 mouse embryos. Because 3 × 3 SR was performed, the image acquisition time can be reduced from 15 h to 1.7 h. Compared to previous SR methods without TSE modeling, the proposed method achieved the best quantitative imaging metrics for both retrospective and prospective evaluations and achieved the best imaging-quality expert scores for prospective evaluation. The proposed T2 -deblurring method improved accuracy and image quality of deep learning-based SR of TSE MRI. This method has the potential to accelerate TSE image acquisition by a factor of up to 9.

Multimodal magnetic resonance images (e.g., T1-weighted image (TIWI) and T2-weighted image (T2WI)) are used for accurate medical imaging analysis. Different modal images have different resolution depending on pulse sequence parameters under limited data acquisition time. Therefore, interpolation methods are used to match the low-resolution (LR) image with the high-resolution (HR) image. However, the interpolation causes blurring that affects analysis accuracy. Although some recent works such as non-local-means (NLM) filter have manifested impressive super-resolution (SR) performance with available HR modal images, the filter has high computational cost. Therefore, we propose a fast SR framework with iterative-guided back projection, which incorporates iterative back projection with a guided filter (GF) method for resolution enhancement of LR images (e.g., T2WI) by referring HR images in another modality image (e.g., T1WI). The proposed method not only achieves both high accuracy than conventional interpolation methods and original GF and computational efficiency by applying an integral 3D image technique. In addition, although the proposed method is slightly inferior in accuracy visually than the state-of-the-art NLM filter, it can run 22 times faster than the state-of-theart method in expanding three times in the slice-select direction from 180 × 216 × 60 voxels to 180 × 216 × 180 voxels. The computational time of our method is about 1 min only. Therefore, the proposed method will be applied to various applications in practice, including not only multimodal MR images but also multimodal image analysis such as computed tomography (CT) and positron emission tomography (PET).

This study explored the integration of super-resolution (SR) image reconstruction technology into environmental art design, addressing key challenges and highlighting its potential to enhance visual quality, design efficiency, ecological awareness, user satisfaction, and cultural heritage preservation. By examining the principles and models of SR technology, the study demonstrated its ability to significantly improve the aesthetic and functional standards of environmental art, contributing to more sustainable and culturally rich designs. The analysis and practical applications presented underscored the significance of SR technology in advancing both technological and artistic progress. Future developments in SR technology promise to further transform environmental art design, creating more dynamic, sustainable, and visually compelling environments.

We present experimental results of digital super resolution (DSR) techniques on low resolution data collected using PANOPTES, a multi-aperture miniature folded imaging architecture. The flat form factor of PANOPTES architecture results in an optical system that is heavily blurred with space variant PSF which makes super resolution challenging. We also introduce a new DSR method called SRUM (Super-Resolution with Unsharpenning Mask) which can efficiently highlight edges by embedding an unsharpenning mask to the cost function. This has much better effect than just applying the mask after all iterations as a post-processing step.

Learning-based methods have been becoming the mainstream of single image super resolution (SR) technologies. It is effective to generate a high-resolution image from a single low resolution input. However, the quality of training data and the computational demand are two main problems. We propose a novel process framework for single image SR tasks aiming at these two problems, which consists of blur kernel estimation (BKE) and dictionary learning. BKE is utilized for improving the quality of training samples and compatibility between training samples and test samples, which is realized by minimizing the dissimilarity between cross-scale patches iteratively (intuitively be equivalent to maximizing the similarity of cross-scale patches). A selective patch processing (SPP) strategy is adopted in BKE and sparse recovery to reduce the number of patches needed to be processed. The fact that nature images usually contain continuity and discontinuity simultaneously ensures the feasibility of SPP. The experimental results show that our method produces more precise estimation for blurring kernel and better SR effect than several state-of-the-art SR algorithms on equal conditions, but needs much less computation time.

Super resolution is a technology that converts low-resolution images into high-resolution images, and super resolution technologies using deep learning have recently been studied. The SRCNN model was first studied as a deep learning-based super-resolution technology, and the SRCNN model performs early up-sampling and learning using interpolation to perform super-resolution. In this paper, the best performance early up-sampling interpolation was investigated through the performance comparison of the early up-sampling method. Through this, it was found that the bicubic interpolation method derived good performance from up to 35% to at least 17% compared to other interpolation methods.

During the process of imagery, the factors including the motion between earth and the platform, atmosphere disturbance, out of focus, non-ideal sampling and so on, all can make the images blurred and degraded. Super resolution technology is the signal processing based method which can detect and remove the blur caused by the imaging system as well as recover spatial frequency information. Super resolution imaging processes one or more low resolution images acquired from the same scene to produce a single higher resolution image with more details.

Multiplexed Superresolution CRISPR Imaging of Chromatin in Living Cells

Over the last years structured illumination digital holographic microscopy (SI-DHM) has been experimentally proved to double the resolution limit in conventional DHM. In SI-DHM, the underlying specimen is illuminated using a spatially varying structured illumination (SI) pattern, which enables super-resolution (SR) images to be retrieved using the proper computational reconstruction process. All these reconstruction methods require the acquisition of at least a couple phase-shifted DHM images. In particular, for a pure sinusoidal pattern, there is a need of recording two phase-shifted DHM images per orientation of the pattern (e.g., 6 images per isotropic SR improvement). Taking advantage of the simultaneous recording of the virtual (e.g., conjugated) image in the raw DHM image, here we present a novel computational method to reconstruct an isotropic SR image using one acquisition per pattern’s orientation (e.g. total 3 images per isotropic improvement). Because our proposed method shows a 50% reduction in the data acquisition and, therefore, acquisition time, we believe that our method should increase the utility of SI-DHM in live-cell imaging. We have validated our method using simulated and results.

Remote sensing images are becoming increasingly widespread in military, earth resource exploration. Because of the limitation of a single sensor, we can obtain high spatial resolution grayscale panchromatic (PAN) images and low spatial resolution color multispectral (MS) images. Therefore, an important issue is to obtain a color image with high spatial resolution when there is only a PAN image at the input. The existing methods improve spatial resolution using super-resolution (SR) technology and spectral recovery using colorization technology. However, the SR technique cannot improve the spectral resolution, and the colorization technique cannot improve the spatial resolution. Moreover, the pansharpening method needs two registered inputs and can not achieve SR. As a result, an integrated approach is expected. We designed a novel multi-function model (MFmamba) to realize the tasks of SR, spectral recovery, joint SR and spectral recovery through three different inputs. Firstly, MFmamba utilizes UNet++ as the backbone, and a Mamba Upsample Block (MUB) is combined with UNet++. Secondly, a Dual Pool Attention (DPA) is designed to replace the skip connection in UNet++. Finally, a Multi-scale Hybrid Cross Block (MHCB) is proposed for initial feature extraction. Many experiments show that MFmamba is competitive in evaluation metrics and visual results and performs well in the three tasks when only the input PAN image is used.

The function of the neuronal synapse depends on the dynamics and interactions of individual molecules at the nanoscale. With the development of single-molecule super-resolution microscopy over the last decades, researchers now have a powerful and versatile imaging tool for mapping the molecular mechanisms behind the biological function. However, imaging of thicker samples, such as mammalian cells and tissue, in all three dimensions is still challenging due to increased fluorescence background and imaging volumes. The combination of single-molecule imaging with light sheet illumination is an emerging approach that allows for imaging of biological samples with reduced fluorescence background, photobleaching, and photodamage. In this review, we first present a brief overview of light sheet illumination and previous super-resolution techniques used for imaging of neurons and synapses. We then provide an in-depth technical review of the fundamental concepts and the current state of the art in the fields of three-dimensional single-molecule tracking and super-resolution imaging with light sheet illumination. We review how light sheet illumination can improve single-molecule tracking and super-resolution imaging in individual neurons and synapses, and we discuss emerging perspectives and new innovations that have the potential to enable and improve single-molecule imaging in brain tissue.

Diverse constraints on image acquisition environment often limit the resolution in cross-slice direction of Magnetic Resonance (MR) image volume, which does not meet the requirement of isotropic 3D MR images in accurate medical diagnosis. This paper proposes an algorithm to restore isotropic 3D MR images from anisotropic 2D multi-slice volumes, by preserving the MR details that play significant role in medical diagnosis. The MR image details are preserved using dictionaries, which are learned using fine to coarse patch details, extracted from different scales of MR image. Learned dictionaries provide detail information for restoring MR patch details. Furthermore, a constraint is used to preserve edges within the restored MR image by minimizing an energy cost. Here, the constraint is weighted adaptively according to the dominant edge orientation of the image, to preserve the details along different orientations effectively. Experimental results demonstrate the ability of our approach to preserve MR image details.