Perceptual Extreme Super Resolution Network with Receptive Field Block

Shang, Taizhang; Dai, Qiuju; Zhu, Shengchen; Yang, Tong; Guo, Yandong

doi:10.1109/cvprw50498.2020.00228

Cited by 78 publications

(55 citation statements)

References 22 publications

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“…Among many super-resolution adversarial neural networks, ESRGAN with a receptive field module is superior to other methods in restoring high-frequency details and maintaining content consistency. Receiving field block (RFB)-ESRGAN [ 19 ] can obtain more detailed information for brain MRI ( Table 1 ). Therefore, we adopted RFB-ESRGAN in the first reconstruction work.…”

Section: Main Methods Of Reconstructionmentioning

confidence: 99%

“…We use RFB-ESRGAN [ 19 ] for the first MRI reconstruction. After that, half a number of the MRI slices are reorganized to obtain an image with much noise and many missing values.…”

Section: Main Methods Of Reconstructionmentioning

confidence: 99%

“…FSRCNN [27] is enhanced method based on SRCNN, and EDSR is first method of using deep residual network [15]. We use RFB-ESRGAN [19] for the first MRI reconstruction. After that, half a number of the MRI slices are reorganized to obtain an image with much noise and many missing values.…”

Section: Main Idea and Processesmentioning

confidence: 99%

“…MRI super-resolution research attempted to combine convolutional neural networks for super-resolution reconstruction [ 5 , 7 , 8 , 9 ]. With the introduction of residual networks [ 14 , 15 , 16 , 17 , 18 , 19 ], super-resolution technology based on deep learning began to develop into deeper networks [ 14 , 15 , 16 ]. However, the reconstructed images are usually too smooth and lack visual authenticity, depending on the mean square error (MSE) loss function.…”

Section: Introductionmentioning

confidence: 99%

“…However, the reconstructed images are usually too smooth and lack visual authenticity, depending on the mean square error (MSE) loss function. With the introduction of generative adversarial networks (GANs) [ 20 ], the blurring image problem is improved, and SISR has been developed to reduce perceived loss [ 19 ]. MRI super-resolution reconstruction is focused on GANs to restore more textural details and high-frequency information rather than overall clarity.…”

Section: Introductionmentioning

confidence: 99%

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3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

Zhang

Shinomiya

Yoshida

2021

Sensors

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The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field strength and scanning time. Scanners with lower field strengths have the disadvantages of a low resolution and high imaging cost, and scanning takes a long time. The traditional super-resolution reconstruction method based on MRI generally states an optimization problem in terms of prior information. It solves the problem using an iterative approach with a large time cost. Many methods based on deep learning have emerged to replace traditional methods. MRI super-resolution technology based on deep learning can effectively improve MRI resolution through a three-dimensional convolutional neural network; however, the training costs are relatively high. In this paper, we propose the use of two-dimensional super-resolution technology for the super-resolution reconstruction of MRI images. In the first reconstruction, we choose a scale factor of 2 and simulate half the volume of MRI slices as input. We utilize a receiving field block enhanced super-resolution generative adversarial network (RFB-ESRGAN), which is superior to other super-resolution technologies in terms of texture and frequency information. We then rebuild the super-resolution reconstructed slices in the MRI. In the second reconstruction, the image after the first reconstruction is composed of only half of the slices, and there are still missing values. In our previous work, we adopted the traditional interpolation method, and there was still a gap in the visual effect of the reconstructed images. Therefore, we propose a noise-based super-resolution network (nESRGAN). The noise addition to the network can provide additional texture restoration possibilities. We use nESRGAN to further restore MRI resolution and high-frequency information. Finally, we achieve the 3D reconstruction of brain MRI images through two super-resolution reconstructions. Our proposed method is superior to 3D super-resolution technology based on deep learning in terms of perception range and image quality evaluation standards.

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Section: Main Methods Of Reconstructionmentioning

confidence: 99%

“…We use RFB-ESRGAN [ 19 ] for the first MRI reconstruction. After that, half a number of the MRI slices are reorganized to obtain an image with much noise and many missing values.…”

Section: Main Methods Of Reconstructionmentioning

confidence: 99%

Section: Main Idea and Processesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

Zhang

Shinomiya

Yoshida

2021

Sensors

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Hyper‐Sampling Imaging by Measurement of Intra‐Pixel Quantum Efficiency Using Steady Wave Field

Xue,

Shang,

Zhang

et al. 2024

Laser & Photonics Reviews

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The transition from optical film to digital image sensors (DIS) in imaging systems has brought great convenience to human life. However, the sampling resolution of DIS is considerably lower than that of optical film due to the limitation that the pixels are significantly larger than the silver halide molecules. How to break DIS's sampling limit and achieve high‐resolution imaging is highly desired for imaging applications. In the research, a novel mechanism that allows for a significant reduction in the smallest sampling unit of DIS to as small as 1/16th of a pixel, or even smaller, through measuring the intra‐pixel quantum efficiency for the first time and recomputing the image — a technique referred to as hyper‐sampling imaging (HSI) is developed. Employing the HSI method, the physical sampling resolution of regular DIS can be enhanced by 4 × 4 times or potentially higher, and detailed object information can be acquired. The HSI method has undergone rigorous testing in real‐world imaging scenarios, demonstrating its robustness and efficiency in overcoming the sampling constraints of conventional DIS. This advancement is particularly beneficial for applications such as remote sensing, long‐range reconnaissance, and astronomical observations, where the ability to capture fine details is paramount.

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Deep‐learning based super‐resolution for 3D isotropic coronary MR angiography in less than a minute

Küstner

Muñoz

Psenicny

et al. 2021

Magnetic Resonance in Med

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Purpose To develop and evaluate a novel and generalizable super‐resolution (SR) deep‐learning framework for motion‐compensated isotropic 3D coronary MR angiography (CMRA), which allows free‐breathing acquisitions in less than a minute. Methods Undersampled motion‐corrected reconstructions have enabled free‐breathing isotropic 3D CMRA in ~5‐10 min acquisition times. In this work, we propose a deep‐learning–based SR framework, combined with non‐rigid respiratory motion compensation, to shorten the acquisition time to less than 1 min. A generative adversarial network (GAN) is proposed consisting of two cascaded Enhanced Deep Residual Network generator, a trainable discriminator, and a perceptual loss network. A 16‐fold increase in spatial resolution is achieved by reconstructing a high‐resolution (HR) isotropic CMRA (0.9 mm3 or 1.2 mm3) from a low‐resolution (LR) anisotropic CMRA (0.9 × 3.6 × 3.6 mm3 or 1.2 × 4.8 × 4.8 mm3). The impact and generalization of the proposed SRGAN approach to different input resolutions and operation on image and patch‐level is investigated. SRGAN was evaluated on a retrospective downsampled cohort of 50 patients and on 16 prospective patients that were scanned with LR‐CMRA in ~50 s under free‐breathing. Vessel sharpness and length of the coronary arteries from the SR‐CMRA is compared against the HR‐CMRA. Results SR‐CMRA showed statistically significant (P < .001) improved vessel sharpness 34.1% ± 12.3% and length 41.5% ± 8.1% compared with LR‐CMRA. Good generalization to input resolution and image/patch‐level processing was found. SR‐CMRA enabled recovery of coronary stenosis similar to HR‐CMRA with comparable qualitative performance. Conclusion The proposed SR‐CMRA provides a 16‐fold increase in spatial resolution with comparable image quality to HR‐CMRA while reducing the predictable scan time to <1 min.

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Perceptual Extreme Super Resolution Network with Receptive Field Block

Cited by 78 publications

References 22 publications

3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

3D MRI Reconstruction Based on 2D Generative Adversarial Network Super-Resolution

Hyper‐Sampling Imaging by Measurement of Intra‐Pixel Quantum Efficiency Using Steady Wave Field

Deep‐learning based super‐resolution for 3D isotropic coronary MR angiography in less than a minute

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