LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations

Shi, Feng; Cheng, Jian; Wang, Li; Yap, Pew-Thian; Shen, Dinggang

doi:10.1109/tmi.2015.2437894

Cited by 262 publications

(214 citation statements)

References 28 publications

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“…After removing seriously polluted bands and cropping images for each data set, the HSI cube used for the experiments are of 256 × 256 × 146, 256 × 256 × 140 and 256 × 256 × 140, respectively. To thoroughly evaluate the performance of the proposed approach, we considered three popular super-resolution methods for comparison, that is, the nonlocal autoregressive model (NARM) proposed by [21], the spatial-spectral group sparsity method (SSGS) proposed by [19], the low-rank and total variation regulariztions (LRTV) method proposed by [23]. We also considered the nearest neighbor interpolation (NN) method that is used to achieve the upsampled HSI for comparison.…”

Section: Experimental Studymentioning

confidence: 99%

“…In this paper, following the ideas of our preliminary work [22] and the work [23] for MRI super-resolution, we consider the HSI cube as the tensor with three modes, namely, width, height, and band, and then exploit the underlying structures in both spatial and spectral domain by using direct tensor modeling techniques to achieve the spatial resolution enhancement. Precisely, the spectral bands of an HSI commonly have global strong correlations, and for each local fullband patch of an HSI (which is stacked by patches at the same location of HSI over all bands), there are many same size fullband patches similar to it; this spatial-and-spectral correlation is modeled by a nonconvex low-rank tensor penalty.…”

Section: Introductionmentioning

confidence: 99%

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Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

et al. 2017

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Hyperspectral image (HSI) possesses three intrinsic characteristics: the global correlation across spectral domain, the nonlocal self-similarity across spatial domain, and the local smooth structure across both spatial and spectral domains. This paper proposes a novel tensor based approach to handle the problem of HSI spatial super-resolution by modeling such three underlying characteristics. Specifically, a noncovex tensor penalty is used to exploit the former two intrinsic characteristics hidden in several 4D tensors formed by nonlocal similar patches within the 3D HSI. In addition, the local smoothness in both spatial and spectral modes of the HSI cube is characterized by a 3D total variation (TV) term. Then, we develop an effective algorithm for solving the resulting optimization by using the local linear approximation (LLA) strategy and the alternative direction method of multipliers (ADMM). A series of experiments are carried out to illustrate the superiority of the proposed approach over some state-of-the-art approaches.

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Section: Experimental Studymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

et al. 2017

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“…In the past few decades, many methods have been proposed for HR image reconstruction in medical imaging and computer vision areas, which can be categorized into reconstruction-based methods [4]–[13], and example-based methods [14]–[38]. …”

Section: Introductionmentioning

confidence: 99%

“…In the reconstruction-based methods, HR image reconstruction is considered as an inverse problem of recovering a HR image by assuming some degradation factors such as blurring and down-sampling, based on some prior knowledge about the observation model [4]. Since the details are missing in the LR image, one LR image may correspond to many HR images.…”

Section: Introductionmentioning

confidence: 99%

Reconstruction of 7T-Like Images From 3T MRI

Bahrami

Shi

Zong

et al. 2016

IEEE Trans. Med. Imaging

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In the recent MRI scanning, ultra-high-field (7T) MR imaging provides higher resolution and better tissue contrast compared to routine 3T MRI, which may help in more accurate and early brain diseases diagnosis. However, currently, 7T MRI scanners are more expensive and less available at clinical and research centers. These motivate us to propose a method for the reconstruction of images close to the quality of 7T MRI, called 7T-like images, from 3T MRI, to improve the quality in terms of resolution and contrast. By doing so, the post-processing tasks, such as tissue segmentation, can be done more accurately and brain tissues details can be seen with higher resolution and contrast. To do this, we have acquired a unique dataset which includes paired 3T and 7T images scanned from same subjects, and then propose a hierarchical reconstruction based on group sparsity in a novel multi-level Canonical Correlation Analysis (CCA) space, to improve the quality of 3T MR image to be 7T-like MRI. First, overlapping patches are extracted from the input 3T MR image. Then, by extracting the most similar patches from all the aligned 3T and 7T images in the training set, the paired 3T and 7T dictionaries are constructed for each patch. It is worth noting that, for the training, we use pairs of 3T and 7T MR images from each training subject. Then, we propose multi-level CCA to map the paired 3T and 7T patch sets to a common space to increase their correlations. In such space, each input 3T MRI patch is sparsely represented by the 3T dictionary and then the obtained sparse coefficients are used together with the corresponding 7T dictionary to reconstruct the 7T-like patch. Also, to have the structural consistency between adjacent patches, the group sparsity is employed. This reconstruction is performed with changing patch sizes in a hierarchical framework. Experiments have been done using 13 subjects with both 3T and 7T MR images. The results show that our method outperforms previous methods and is able to recover better structural details. Also, to place our proposed method in a medical application context, we evaluated the influence of post-processing methods such as brain tissue segmentation on the reconstructed 7T-like MR images. Results show that our 7T-like images lead to higher accuracy in segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF), and skull, compared to segmentation of 3T MR images.

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“…Here, we focus on the SRR problem for single input image with no training sets. Currently, existing single image SRR methods are generally proposed for 3D structural MR images, while not developed and evaluated in DWI [8, 9, 14, 15]. Note that diffusion-weighted images are in 4-dimensional (4D) space, and more sensitive to noise, especially for the images with large diffusion factor b-value.…”

Section: Introductionmentioning

confidence: 99%

Super-Resolution Reconstruction of Diffusion-Weighted Images Using 4D Low-Rank and Total Variation

Shi

Cheng

Wang

et al. 2016

Mathematics and Visualization

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Diffusion-weighted imaging (DWI) provides invaluable information in white matter microstructure and is widely applied in neurological applications. However, DWI is largely limited by its relatively low spatial resolution. In this paper, we propose an image post-processing method, referred to as super-resolution reconstruction, to estimate a high spatial resolution DWI from the input low-resolution DWI, e.g., at a factor of 2. Instead of requiring specially designed DWI acquisition of multiple shifted or orthogonal scans, our method needs only a single DWI scan. To do that, we propose to model both the blurring and downsampling effects in the image degradation process where the low-resolution image is observed from the latent high-resolution image, and recover the latent high-resolution image with the help of two regularizations. The first regularization is 4-dimensional (4D) low-rank, proposed to gather self-similarity information from both the spatial domain and the diffusion domain of 4D DWI. The second regularization is total variation, proposed to depress noise and preserve local structures such as edges in the image recovery process. Extensive experiments were performed on 20 subjects, and results show that the proposed method is able to recover the fine details of white matter structures, and outperform other approaches such as interpolation methods, non-local means based upsampling, and total variation based upsampling.

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LRTV: MR Image Super-Resolution With Low-Rank and Total Variation Regularizations

Cited by 262 publications

References 28 publications

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

Hyperspectral Image Super-Resolution via Nonlocal Low-Rank Tensor Approximation and Total Variation Regularization

Reconstruction of 7T-Like Images From 3T MRI

Super-Resolution Reconstruction of Diffusion-Weighted Images Using 4D Low-Rank and Total Variation

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