MRI super-resolution via realistic downsampling with adversarial learning

Single Image Super-Resolution (SISR) using Convolutional Neural Networks (CNNs) for many applications in <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">supervised</i> manner has resulted in state-of-the-art results. Such supervised models achieve remarkable accuracy; albeit their poor generalization ability for real-world Low-Resolution (LR) images. Supervised training in many SR works involves synthetically generated LR images from its corresponding High-Resolution (HR) images. As the distribution of such LR observation is relatively different from that of real LR image, the supervised training in SISR task results degradation when applied on real-world data. SISR has been scaled to real-world data recently by posing the unsupervised problem into a supervised one through learning the distribution of noisy LR observation first, following which supervised training is performed to obtain the SR image. It therefore involves two steps where the accuracy of SR image relies on how closely the LR’s distribution is learnt in the first step. In this work, we overcome such limitation by introducing unsupervised denoising network to transform real noisy LR image to clean image and then pre-trained SR network is utilised to increase the spatial resolution of cleaned LR image to generate SR image. Thus, instead of evaluating the denoised image in LR space to train the denoising network, we inspect the denoised image in SR space which allows to overcome the SR network’s generalization problem. The proposed Unsupervised Denoising framework for Super-Resolution ( <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">UDSR</i> ) is validated on real-world datasets (NTIRE-2020 Real-World SR Challenge validation and testing dataset (Track-1)) by comparing it with many recent unsupervised SISR methods. The performance of denoising and SR networks is superior in terms of various perceptual indices such as Perceptual Index (PI) and Ma Score in addition to numerous non-references metrics.

Learning based single image super-resolution (SISR) for real-world images has been an active research topic yet a challenging task, due to the lack of paired low-resolution (LR) and high-resolution (HR) training images. Most of the existing unsupervised real-world SISR methods adopt a two-stage training strategy by synthesizing realistic LR images from their HR counterparts first, then training the super-resolution (SR) models in a supervised manner. However, the training of image degradation and SR models in this strategy are separate, ignoring the inherent mutual dependency between downscaling and its inverse upscaling process. Additionally, the ill-posed nature of image degradation is not fully considered. In this paper, we propose an image downscaling and SR model dubbed as SDFlow, which simultaneously learns a bidirectional many-to-many mapping between real-world LR and HR images unsupervisedly. The main idea of SDFlow is to decouple image content and degradation information in the latent space, where content information distribution of LR and HR images is matched in a common latent space. Degradation information of the LR images and the high-frequency information of the HR images are fitted to an easy-to-sample conditional distribution. Experimental results on real-world image SR datasets indicate that SDFlow can generate diverse realistic LR and SR images both quantitatively and qualitatively.

Deep Feature Blend Attention: A New Frontier in Super Resolution Image Generation

The general image super-resolution reconstruction (SRR) methods based on sparse representation utilizes the one-stage high and low resolution dictionary pairs to reconstruct a high resolution image, and this method can not restore much image detail information. To solve this detect, two-stage high and low resolution dictionaries are explored here. The goal of exploiting the two-stage dictionaries is to reconstruct the difference image between the original high resolution image and the reconstructed image obtained by using the one-stage dictionaries. In learning two-stage dictionaries, the difference image is used as the high resolution (HR) image, and the first-order and second-order gradient feature images of the one-stage reconstructed images are used as the low resolution (LR) images. Then, the two-stage dictionaries are learned by K- singular value decomposition (K-SVD) method. In test, an artificial and a real LR image are used, and simulation results show that, compared with other learning-based methods, our method proposed has remarkable improvement in PSNR and visual effect.

This paper proposes a new algorithm to integrate image registration into image super-resolution (SR). Image SR is a process to reconstruct a high-resolution (HR) image by fusing multiple low-resolution (LR) images. A critical step in image SR is accurate registration of the LR images or, in other words, effective estimation of motion parameters. Conventional SR algorithms assume either the estimated motion parameters by existing registration methods to be error-free or the motion parameters are known a priori. This assumption, however, is impractical in many applications, as most existing registration algorithms still experience various degrees of errors, and the motion parameters among the LR images are generally unknown a priori. In view of this, this paper presents a new framework that performs simultaneous image registration and HR image reconstruction. As opposed to other current methods that treat image registration and HR reconstruction as disjoint processes, the new framework enables image registration and HR reconstruction to be estimated simultaneously and improved progressively. Further, unlike most algorithms that focus on the translational motion model, the proposed method adopts a more generic motion model that includes both translation as well as rotation. An iterative scheme is developed to solve the arising nonlinear least squares problem. Experimental results show that the proposed method is effective in performing image registration and SR for simulated as well as real-life images.

Image super-resolution is of great interest in medical imaging field. However, different from natural images studied in computer vision field, the low-resolution (LR) medical imaging data is often a stack of high-resolution (HR) 2D slices with large slice thickness. Consequently, the goal of super-resolution for medical imaging data is to reconstruct the missing slice(s) between any two consecutive slices. Since some modalities (e.g., T1-weighted MR image) are often acquired with high-resolution (HR) image, it is intuitive to harness the prior self-similarity information in the HR image for guiding the super-resolution of LR image (e.g., T2-weighted MR image). The conventional way is to find the profile of patchwise self-similarity in the HR image and then use it to reconstruct the missing information at the same location of LR image. However, the local morphological patterns could vary significantly across the LR and HR images, due to the use of different imaging protocols. Therefore, such direct (un-supervised) adaption of self-similarity profile from HR image is often not effective in revealing the actual information in the LR image. To this end, we propose to employ the existing image information in the LR image to supervise the estimation of self-similarity profile by requiring it not only being optimal in representing patches in the HR image, but also producing less reconstruction errors for the existing image information in the LR image. Moreover, to make the anatomical structures spatially consistent in the reconstructed image, we simultaneously estimate the self-similarity profiles for a stack of patches across consecutive slices by solving a group sparse patch representation problem. We have evaluated our proposed super-resolution method on both simulated brain MR images and real patient images with multiple sclerosis lesion, achieving promising results with more anatomical details and sharpness.

Acquiring degraded images with paired high-resolution (HR) images is often challenging, impeding the advance of image super-resolution in real-world applications. By generating realistic low-resolution (LR) images with degradation similar to that in real-world scenarios, simulated paired LR-HR data can be constructed for supervised training. However, most of the existing work ignores the degradation uncertainty of the generated realistic LR images, since only one LR image has been generated given an HR image. To address this weakness, we propose learning the degradation uncertainty of generated LR images and sampling multiple LR images from the learned LR image (mean) and degradation uncertainty (variance) and construct LR-HR pairs to train the super-resolution (SR) networks. Specifically, uncertainty can be learned by minimizing the proposed loss based on Kullback-Leibler (KL) divergence. Furthermore, the uncertainty in the feature domain is exploited by a novel perceptual loss; and we propose to calculate the adversarial loss from the gradient information in the SR stage for stable training performance and better visual quality. Experimental results on popular real-world datasets show that our proposed method has performed better than other unsupervised approaches.

Cross-Resolution Person Re-Identification (re-ID) aims to match images with disparate resolutions arising from variations in camera hardware and shooting distances. Most conventional works utilize Super-Resolution (SR) models to recover Low Resolution (LR) images to High Resolution (HR) images. However, because the SR models cannot completely compensate for the missing information in the LR images, there is still a large gap between the HR image recovered from the LR images and the real HR images. To tackle this challenge, we propose a novel Multi-Scale Image- and Feature-Level Alignment (MSIFLA) framework to align the images on multiple resolution scales at both the image and feature level. Specifically, (i) we design a Cascaded Multi-Scale Resolution Reconstruction (CMSR2) module, which is composed of three cascaded Image Reconstruction (IR) networks, and can continuously reconstruct multiple variables of different resolution scales from low to high for each image, regardless of image resolution. The reconstructed images with specific resolution scales are of similar distribution; therefore, the images are aligned on multiple resolution scales at the image level. (ii) We propose a Multi-Resolution Representation Learning (MR2L) module which consists of three-person re-ID networks to encourage the IR models to preserve the ID-discriminative information during training separately. Each re-ID network focuses on mining discriminative information from a specific scale without the disturbance from various resolutions. By matching the extracted features on three resolution scales, the images with different resolutions are also aligned at the feature-level. We conduct extensive experiments on multiple public cross-resolution person re-ID datasets to demonstrate the superiority of the proposed method. In addition, the generalization of MSIFLA in handling cross-resolution retrieval tasks is verified on the UAV vehicle dataset.

In this paper, we demonstrate simple algorithms that project low resolution (LR) images differing in subpixel shifts on a high resolution (HR) also called super resolution (SR) grid. The algorithms are very effective in accuracy as well as time efficiency. A number of spatial interpolation techniques using nearest neighbor, inverse-distance weighted averages, Radial Basis Functions (RBF) etc. are used in projection. For best accuracy of reconstructing SR image by a factor of two requires four LR images differing in four independent subpixel shifts. The algorithm has two steps: i) registration of low resolution images and (ii) shifting the low resolution images to align with reference image and projecting them on high resolution grid based on the shifts of each low resolution image using different interpolation techniques. Experiments are conducted by simulating low resolution images by subpixel shifts and subsampling of original high resolution image and the reconstructing the high resolution images from the simulated low resolution images. The results of accuracy of reconstruction are compared by using mean squared error measure between original high resolution image and reconstructed image. The algorithm was tested on remote sensing images and found to outperform previously proposed techniques such as Iterative Back Projection algorithm (IBP), Maximum Likelihood (ML) algorithms. The algorithms are robust and are not overly sensitive to the registration inaccuracies.

Image super-resolution is a research endeavour that has gained notoriety in computer vision. The research goal is to increase the spatial dimensions of an image using corresponding low-resolution and high-resolution image pairs to enhance the perceptual quality. The challenge of maintaining such perceptual quality lies in developing appropriate algorithms that learn to reconstruct higher-quality images from their lower-resolution counterparts. Recent methods employ deep learning algorithms to reconstruct textural details prevalent in low-resolution images. Since corresponding image pairs are non-trivial to collect, researchers attempt super-resolution by creating synthetic low-resolution representations of high-resolution images. Unfortunately, such methods employ ineffective downscaling operations to achieve synthetic low-resolution images. These methods fail to generalize well on real-world images that may suffer different degradations. A different angle is offered to solve the task of image super-resolution by investigating the plausibility of learning the degradational operation using generative adversarial networks. A two-stage generative adversarial network along with two architectural variations is proposed to solve the task of real-world super-resolution from low-resolution images with unknown degradations. It is demonstrated that learning to downsample images in a weakly supervised manner is an impactful and viable approach for super-resolution.

In this paper we propose a fast approach for edge preserving super-resolution (SR) based on learning of contourlet coefficients. Given a low resolution test image, we first obtain an initial HR estimate i.e., a close approximation to SR image by learning the contourlet coefficients from a training database consisting of low resolution (LR) and high resolution (HR) images. The final SR image is obtained by using a regularization framework in which both the SR and the LR images are modeled as separate homogeneous Markov Random Fields (MRFs). The LR image formation process is modeled as a decimated and noisy version of the SR image and the final cost function is minimized by using a gradient descent method. Novelty of our approach lies in preserving the edges in the final SR image while using a non edge preserving MRF prior. This is definitely advantageous since it avoids the use of discontinuity preserving prior and hence the computationally taxing optimization methods. The edges in the final SR correspond to those learned from the initial HR estimate. The use of MRF on the low resolution image imposes an additional constraint on the final solution and hence we expect a better solution. In addition, we use the initial HR image for estimating the decimation matrix entries as well as for learning the corresponding MRF parameter. We show the effectiveness of the proposed approach by conducting the experiments on images captured using a real camera.

Deep convolutional neural networks (CNNs), trained on corresponding pairs of high- and low-resolution images, achieve state-of-the-art performance in single-image super- resolution and surpass previous signal-processing based approaches. However, their performance is limited when applied to real photographs. The reason lies in their train- ing data: low-resolution (LR) images are obtained by bicu- bic interpolation of the corresponding high-resolution (HR) images. The applied convolution kernel significantly differs from real-world camera-blur. Consequently, while current CNNs well super-resolve bicubic-downsampled LR images, they often fail on camera-captured LR images. To improve generalization and robustness of deep super- resolution CNNs on real photographs, we present a ker- nel modeling super-resolution network (KMSR) that incor- porates blur-kernel modeling in the training. Our pro- posed KMSR consists of two stages: we first build a pool of realistic blur-kernels with a generative adversarial net- work (GAN) and then we train a super-resolution network with HR and corresponding LR images constructed with the generated kernels. Our extensive experimental vali- dations demonstrate the effectiveness of our single-image super-resolution approach on photographs with unknown blur-kernels.

This paper presents a technique that used to enhance the image resolution. The input low resolution (LR) image is decomposed into low frequency (LF) and high frequency (HF) sub-bands by using lifting wavelet transform (LWT) and discrete wavelet transform (DWT). The resolution enhancement is done by interpolating HF sub-bands that are generated using LWT from the LR image. The HF sub-bands of DWT are used to preserve the edges of the images. The operation is done on HF sub-bands of LWT and DWT. The HF sub-bands that are generated from the DWT are interpolated and perform addition to the respective interpolated HF sub-bands of LWT. The HF sub-bands that are modified and the LR image will again interpolate and undergoes the inverse lifting wavelet transform (ILWT) to reconstruct the high resolution (HR) image. This implemented method results efficient over conventional methods.

Zoom based image super-resolution using DCT with LBP as characteristic model

Super-resolution (SR) techniques produce a high resolution (HR) image from a set of low-resolution (LR) undersampled images. Usually, SR problems are posed as estimation problems where the LR images are contaminated by stationary noise. However, in real SR problems is very common to have non-stationary noise due to problems in the registration of the images or outliers. SR methods that address this type of problems are called robust. In this paper we propose a novel robust SR method that employs a median filter directly in the data from the LR images, before proceeding to the interpolation and deblurring steps that are common in SR. We compare this new method with other robust SR methods with synthetic and real data, proving that it outperforms the other methods in both cases.KeywordsProjective MethodMedian FilterRegistration ErrorPepper NoiseIrregular SampleThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.