Kun Yang^1,2, Lei Zhao¹, Xianghui Wang¹, Mingyang Zhang¹, Linyan Xue^1,2, Shuang Liu^1,2, Kun Liu^1,2,3,*

CMC-Computers, Materials & Continua, Vol.75, No.3, pp. 5159-5176, 2023, DOI:10.32604/cmc.2023.036401

Abstract The diagnosis of COVID-19 requires chest computed tomography (CT). High-resolution CT images can provide more diagnostic information to help doctors better diagnose the disease, so it is of clinical importance to study super-resolution (SR) algorithms applied to CT images to improve the resolution of CT images. However, most of the existing SR algorithms are studied based on natural images, which are not suitable for medical images; and most of these algorithms improve the reconstruction quality by increasing the network depth, which is not suitable for machines with limited resources. To alleviate these issues, we propose a residual feature attentional fusion… More >

Liwei Deng¹, Yuanzhi Zhang¹, Xin Yang^2,*, Sijuan Huang², Jing Wang^3,*

CMC-Computers, Materials & Continua, Vol.75, No.2, pp. 2671-2684, 2023, DOI:10.32604/cmc.2023.036642

Abstract High-resolution medical images have important medical value, but are difficult to obtain directly. Limited by hardware equipment and patient’s physical condition, the resolution of directly acquired medical images is often not high. Therefore, many researchers have thought of using super-resolution algorithms for secondary processing to obtain high-resolution medical images. However, current super-resolution algorithms only work on a single scale, and multiple networks need to be trained when super-resolution images of different scales are needed. This definitely raises the cost of acquiring high-resolution medical images. Thus, we propose a multi-scale super-resolution algorithm using meta-learning. The algorithm combines a meta-learning approach with… More >

Wazir Muhammad¹, Zuhaibuddin Bhutto^2,*, Salman Masroor^3,4, Murtaza Hussain Shaikh⁵, Jalal Shah², Ayaz Hussain¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.2, pp. 1121-1142, 2023, DOI:10.32604/cmes.2023.021438

Abstract Medical image super-resolution is a fundamental challenge due to absorption and scattering in tissues. These challenges are increasing the interest in the quality of medical images. Recent research has proven that the rapid progress in convolutional neural networks (CNNs) has achieved superior performance in the area of medical image super-resolution. However, the traditional CNN approaches use interpolation techniques as a preprocessing stage to enlarge low-resolution magnetic resonance (MR) images, adding extra noise in the models and more memory consumption. Furthermore, conventional deep CNN approaches used layers in series-wise connection to create the deeper mode, because this later end layer cannot… More >

Safi Ullah^1,2, Seong-Ho Song^1,*

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2999-3014, 2023, DOI:10.32604/cmc.2023.032326

Abstract Due to highly underdetermined nature of Single Image Super-Resolution (SISR) problem, deep learning neural networks are required to be more deeper to solve the problem effectively. One of deep neural networks successful in the Super-Resolution (SR) problem is ResNet which can render the capability of deeper networks with the help of skip connections. However, zero padding (ZP) scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data increases. In this paper. we consider the ResNet with Partial Convolution based Padding… More >

Yingying Li¹, Hang Jiang¹, Lianjie Yu¹, Jianfeng Li^1,2,*

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.3, pp. 2123-2136, 2023, DOI:10.32604/cmes.2022.021407

Abstract To improve the estimation accuracy, a novel time delay estimation (TDE) method based on the closed-form offset compensation is proposed. Firstly, we use the generalized cross-correlation with phase transform (GCC-PHAT) method to obtain the initial TDE. Secondly, a signal model using normalized cross spectrum is established, and the noise subspace is extracted by eigenvalue decomposition (EVD) of covariance matrix. Using the orthogonal relation between the steering vector and the noise subspace, the first-order Taylor expansion is carried out on the steering vector reconstructed by the initial TDE. Finally, the offsets are compensated via simple least squares (LS). Compared to other… More >

Israa Ismail^1,*, Mohamed Meselhy Eltoukhy^1,2, Ghada Eltaweel¹

Computer Systems Science and Engineering, Vol.45, No.1, pp. 167-181, 2023, DOI:10.32604/csse.2023.028906

Abstract Super-resolution techniques are used to reconstruct an image with a high resolution from one or more low-resolution image(s). In this paper, we proposed a single image super-resolution algorithm. It uses the nonlocal mean filter as a prior step to produce a denoised image. The proposed algorithm is based on curvelet transform. It converts the denoised image into low and high frequencies (sub-bands). Then we applied a multi-dimensional interpolation called Lancozos interpolation over both sub-bands. In parallel, we applied sparse representation with over complete dictionary for the denoised image. The proposed algorithm then combines the dictionary learning in the sparse representation… More >

Tao Li¹, Zhiwei Jiang^1,*, Rui Han², Jinyue Xia³, Yongjun Ren⁴

Intelligent Automation & Soft Computing, Vol.35, No.1, pp. 941-956, 2023, DOI:10.32604/iasc.2023.029644

Abstract A Generative Adversarial Neural (GAN) network is designed based on deep learning for the Super-Resolution (SR) reconstruction task of temperature fields (comparable to downscaling in the meteorological field), which is limited by the small number of ground stations and the sparse distribution of observations, resulting in a lack of fineness of data. To improve the network’s generalization performance, the residual structure, and batch normalization are used. Applying the nearest interpolation method to avoid over-smoothing of the climate element values instead of the conventional Bicubic interpolation in the computer vision field. Sub-pixel convolution is used instead of transposed convolution or interpolation… More >

Mahmoud M. Khattab^1,2,*, Akram M Zeki¹, Ali A. Alwan³, Belgacem Bouallegue², Safaa S. Matter⁴, Abdelmoty M. Ahmed²

Computer Systems Science and Engineering, Vol.44, No.1, pp. 35-54, 2023, DOI:10.32604/csse.2023.025251

Abstract The prime purpose for the image reconstruction of a multi-frame super-resolution is to reconstruct a higher-resolution image through incorporating the knowledge obtained from a series of relevant low-resolution images, which is useful in numerous fields. Nevertheless, super-resolution image reconstruction methods are usually damaged by undesirable restorative artifacts, which include blurring distortion, noises, and stair-casing effects. Consequently, it is always challenging to achieve balancing between image smoothness and preservation of the edges inside the image. In this research work, we seek to increase the effectiveness of multi-frame super-resolution image reconstruction by increasing the visual information and improving the automated machine perception,… More >

Walid El-Shafai^1,2, Ehab Mahmoud Mohamed^3,4,*, Medien Zeghid^3,5, Anas M. Ali^1,6, Moustafa H. Aly⁷

CMC-Computers, Materials & Continua, Vol.72, No.3, pp. 4879-4896, 2022, DOI:10.32604/cmc.2022.028364

Abstract High-quality medical microscopic images used for diseases detection are expensive and difficult to store. Therefore, low-resolution images are favorable due to their low storage space and ease of sharing, where the images can be enlarged when needed using Super-Resolution (SR) techniques. However, it is important to maintain the shape and size of the medical images while enlarging them. One of the problems facing SR is that the performance of medical image diagnosis is very poor due to the deterioration of the reconstructed image resolution. Consequently, this paper suggests a multi-SR and classification framework based on Generative Adversarial Network (GAN) to… More >

Zhe Wang¹, Liguo Zhang^1,2,*, Tong Shuai³, Shuo Liang³, Sizhao Li^1,4

Journal of New Media, Vol.4, No.1, pp. 27-39, 2022, DOI:10.32604/jnm.2022.027826

Abstract Research shows that deep learning algorithms can effectively improve a single image's super-resolution quality. However, if the algorithm is solely focused on increasing network depth and the desired result is not achieved, difficulties in the training process are more likely to arise. Simultaneously, the function space that can be transferred from a low-resolution image to a high-resolution image is enormous, making finding a satisfactory solution difficult. In this paper, we propose a deep learning method for single image super-resolution. The MDRN network framework uses multi-scale residual blocks and dual learning to fully acquire features in low-resolution images. Finally, these features… More >