A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT

Hatvani, Janka; Basarab, Adrian; Tourneret, Jean-Yves; Gyöngy, Miklós; Kouamé, Denis

doi:10.1109/tmi.2018.2883517

Cited by 42 publications

(45 citation statements)

References 25 publications

(48 reference statements)

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“…Residual errors for the three considered ranks and four materials Groundtruth image for Indian Pines dataset.Materials 4,7,9,14 are marked in red.…”

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confidence: 99%

Hyperspectral Super-Resolution With Coupled Tucker Approximation: Recoverability and SVD-Based Algorithms

Prévost

Usevich

Comon

et al. 2020

IEEE Trans. Signal Process.

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We propose a novel approach for hyperspectral super-resolution, that is based on low-rank tensor approximation for a coupled low-rank multilinear (Tucker) model. We show that the correct recovery holds for a wide range of multilinear ranks. For coupled tensor approximation, we propose two SVD-based algorithms that are simple and fast, but with a performance comparable to the state-of-the-art methods. The approach is applicable to the case of unknown spatial degradation and to the pansharpening problem.

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“…Residual errors for the three considered ranks and four materials Groundtruth image for Indian Pines dataset.Materials 4,7,9,14 are marked in red.…”

mentioning

confidence: 99%

Hyperspectral Super-Resolution With Coupled Tucker Approximation: Recoverability and SVD-Based Algorithms

Prévost

Usevich

Comon

et al. 2020

IEEE Trans. Signal Process.

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“…To our knowledge, there are only a few previous works [1]- [18] that study the super-resolution of CT or MRI images. Similar to some of these previous works [2]- [7], [9], [11]- [18], we approach single-image super-resolution (SISR) of CT and MRI scans using deep convolutional neural networks (CNNs). We propose a CNN architecture composed of 10 convolutional layers and an intermediate sub-pixel convolutional (upscaling) layer [19] that is placed after the first 6 convolutional layers.…”

Section: Introductionmentioning

confidence: 95%

Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

Ionescu

Verga

2020

IEEE Access

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Computed Tomography (CT) scanners that are commonly-used in hospitals and medical centers nowadays produce low-resolution images, e.g. one voxel in the image corresponds to at most onecubic millimeter of tissue. In order to accurately segment tumors and make treatment plans, radiologists and oncologists need CT scans of higher resolution. The same problem appears in Magnetic Resonance Imaging (MRI). In this paper, we propose an approach for the single-image super-resolution of 3D CT or MRI scans. Our method is based on deep convolutional neural networks (CNNs) composed of 10 convolutional layers and an intermediate upscaling layer that is placed after the first 6 convolutional layers. Our first CNN, which increases the resolution on two axes (width and height), is followed by a second CNN, which increases the resolution on the third axis (depth). Different from other methods, we compute the loss with respect to the ground-truth high-resolution image right after the upscaling layer, in addition to computing the loss after the last convolutional layer. The intermediate loss forces our network to produce a better output, closer to the ground-truth. A widely-used approach to obtain sharp results is to add Gaussian blur using a fixed standard deviation. In order to avoid overfitting to a fixed standard deviation, we apply Gaussian smoothing with various standard deviations, unlike other approaches. We evaluate the proposed method in the context of 2D and 3D super-resolution of CT and MRI scans from two databases, comparing it to related works from the literature and baselines based on various interpolation schemes, using 2× and 4× scaling factors. The empirical study shows that our approach attains superior results to all other methods. Moreover, our subjective image quality assessment by human observers reveals that both doctors and regular annotators chose our method in favor of Lanczos interpolation in 97.55% cases for an upscaling factor of 2× and in 96.69% cases for an upscaling factor of 4×. In order to allow others to reproduce our state-of-the-art results, we provide our code as open source at https://github.com/lilygeorgescu/3d-super-res-cnn. INDEX TERMSConvolutional neural networks, single-image super-resolution, CT images, MRI images, medical image super-resolution.

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“…The matrix or tensor decomposition algorithms that yield low-rank approximations have been developed for various image completion and resolution up-scaling problems. Hatvani et al [13] have introduced tensor-factorization-based approach which offers a fast solution without the use of known image pairs or strict prior assumptions to solve ISRR task. To tackle the obstacles of low-rank completion methods, Zdunek et al [14] have proposed to model the incomplete images with overlapping blocks of Tucker decomposition representations.…”

Section: Introductionmentioning

confidence: 99%

Generative Adversarial Network for Image Super-Resolution Combining Texture Loss

Jiang

2020

Applied Sciences

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Objective: Super-resolution reconstruction is an increasingly important area in computer vision. To alleviate the problems that super-resolution reconstruction models based on generative adversarial networks are difficult to train and contain artifacts in reconstruction results, we propose a novel and improved algorithm. Methods: This paper presented TSRGAN (Super-Resolution Generative Adversarial Networks Combining Texture Loss) model which was also based on generative adversarial networks. We redefined the generator network and discriminator network. Firstly, on the network structure, residual dense blocks without excess batch normalization layers were used to form generator network. Visual Geometry Group (VGG)19 network was adopted as the basic framework of discriminator network. Secondly, in the loss function, the weighting of the four loss functions of texture loss, perceptual loss, adversarial loss and content loss was used as the objective function of generator. Texture loss was proposed to encourage local information matching. Perceptual loss was enhanced by employing the features before activation layer to calculate. Adversarial loss was optimized based on WGAN-GP (Wasserstein GAN with Gradient Penalty) theory. Content loss was used to ensure the accuracy of low-frequency information. During the optimization process, the target image information was reconstructed from different angles of high and low frequencies. Results: The experimental results showed that our method made the average Peak Signal to Noise Ratio of reconstructed images reach 27.99 dB and the average Structural Similarity Index reach 0.778 without losing too much speed, which was superior to other comparison algorithms in objective evaluation index. What is more, TSRGAN significantly improved subjective visual evaluations such as brightness information and texture details. We found that it could generate images with more realistic textures and more accurate brightness, which were more in line with human visual evaluation. Conclusions: Our improvements to the network structure could reduce the model’s calculation amount and stabilize the training direction. In addition, the loss function we present for generator could provide stronger supervision for restoring realistic textures and achieving brightness consistency. Experimental results prove the effectiveness and superiority of TSRGAN algorithm.

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A Tensor Factorization Method for 3-D Super Resolution With Application to Dental CT

Cited by 42 publications

References 25 publications

Hyperspectral Super-Resolution With Coupled Tucker Approximation: Recoverability and SVD-Based Algorithms

Hyperspectral Super-Resolution With Coupled Tucker Approximation: Recoverability and SVD-Based Algorithms

Convolutional Neural Networks With Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

Generative Adversarial Network for Image Super-Resolution Combining Texture Loss

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