Temporal super-resolution of echocardiography using a novel high-precision non-polynomial interpolation

Afrakhteh, Sajjad; Jalilian, Hamed; Iacca, Giovanni; Demi, Libertario

doi:10.1016/j.bspc.2022.104003

Cited by 15 publications

(3 citation statements)

References 30 publications

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“…These images are mostly blurred and contain noise due to relative motion, lamination variation, distance variation, and low-quality imaging devices. Applications such as restoration [9], surveillance, and medical imaging systems [10,11] require HR images for recognition and diagnosis, respectively. Although some applications such as Blu-ray movies, video conferencing, and web videos are often in HR, to preserve server storage and bandwidth, they are often stored in LR.…”

Section: Of 13mentioning

confidence: 99%

“…Data works as the fuel for deep learning models; however, collecting large amounts of vehicle license plate data with a uniform spatial resolution and almost the same lightning conditions is a challenging task. For this purpose, we accessed a license plate repository [11], and downloaded 3700 images with various backgrounds and digit colors as a raw dataset. To increase the number of images and diversify the angle of images, we used the data augmentation library "Augmentor" [12].…”

Section: Dataset Acquisitionmentioning

confidence: 99%

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Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications

et al. 2023

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Vehicle license plate images are often low resolution and blurry because of the large distance and relative motion between the vision sensor and vehicle, making license plate identification arduous. The extensive use of expensive, high-quality vision sensors is uneconomical in most cases; thus, images are initially captured and then translated from low resolution to high resolution. For this purpose, several traditional techniques such as bilinear, bicubic, super-resolution convolutional neural network, and super-resolution generative adversarial network (SRGAN) have been developed over time to upgrade low-quality images. However, most studies in this area pertain to the conversion of low-resolution images to super-resolution images, and little attention has been paid to motion de-blurring. This work extends SRGAN by adding an intelligent motion-deblurring method (termed SRGAN-LP), which helps to enhance the image resolution and remove motion blur from the given images. A comprehensive and new domain-specific dataset was developed to achieve improved results. Moreover, maintaining higher quantitative and qualitative results in comparison to the ground truth images, this study upscales the provided low-resolution image four times and removes the motion blur to a reasonable extent, making it suitable for surveillance applications.

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Section: Of 13mentioning

confidence: 99%

Section: Dataset Acquisitionmentioning

confidence: 99%

Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications

et al. 2023

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“…For quantitative evaluation of the proposed method, three parameters ( MAE, PSNR, and SSIM) have been measured [28,29,30]. In fact, with the help of these Algorithm 2: MBBM Input : ϵ, x 0 , k = 0 , η 0 = 1 ,β ∈ (0, 1), λ ∈ (0, 1)…”

Section: Image Quality Metricsmentioning

confidence: 99%

Total Variation Method Based on Modified Barzilai-Borwein Algorithm to Noise Reduction in MRI Images

Jalilian

2023

Preprint

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Magnetic resonance imaging (MRI), notwithstanding the vital information they provide , introduces issues in diagnostic work due to inherent noise. The poor signal-to-noise ratio (SNR) in MRI images demonstrates the importance of post-processing procedures. There are numerous strategies for reducing noise, but there is still a need for a solution that has both high accuracy and a fast convergence speed. In this study, we present a total variation (TV) method for noise reduction in MRI images utilizing a modified Barzilai-Borwein method(MBBM) algorithm. The proposed method is tested on three noisy and blurred images (IXI data set 3.1). The findings reveal that the reconstructed images have less noise and sharper edges. In comparison to similar existing iterative methods, Gradient projection method(GP) and Barzilai-Borwein method(BBM), the proposed method achieves higher accuracy in a substantially smaller number of iterations. The MBBM technique enhanced the quality metrics, Peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) for the first data set (data 2a). For instance, the PSNR and SSIM for noise level 0.06 improved from 2.62 and 0.22 to 33.12 and 0.90, respectively. This improvement in rating criteria was also seen in the next two experiments(data 2b and data 2c). When compared to similar methods, experimental results reveal that the suggested method is faster, more precise, more successful at preserving edges, and less compu-tationally demanding. Consequently, the presented method is effective and reliable both quantitatively and qualitatively.

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Total variation method based on modified Barzilai–Borwein algorithm to noise reduction in MRI images

Jalilian

2023

J Supercomput

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Temporal super-resolution of echocardiography using a novel high-precision non-polynomial interpolation

Cited by 15 publications

References 30 publications

Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications

Intelligent Image Super-Resolution for Vehicle License Plate in Surveillance Applications

Total Variation Method Based on Modified Barzilai-Borwein Algorithm to Noise Reduction in MRI Images

Total variation method based on modified Barzilai–Borwein algorithm to noise reduction in MRI images

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