Jianwei Zhang, Zhenxing Wang, Yuhui Zheng, Guoqing Zhang^*

Journal of New Media, Vol.3, No.1, pp. 29-39, 2021, DOI:10.32604/jnm.2021.018383

Abstract Image super resolution is an important field of computer research. The current mainstream image super-resolution technology is to use deep learning to mine the deeper features of the image, and then use it for image restoration. However, most of these models mentioned above only trained the images in a specific scale and do not consider the relationships between different scales of images. In order to utilize the information of images at different scales, we design a cascade network structure and cascaded super-resolution convolutional neural networks. This network contains three cascaded FSRCNNs. Due to each sub FSRCNN can process a specific… More >

CHEN CAI¹, HAO SUN², LIANG HU³, ZHICHAO FAN^1,*

BIOCELL, Vol.45, No.2, pp. 229-257, 2021, DOI:10.32604/biocell.2021.014338

Abstract Integrin molecules are transmembrane αβ heterodimers involved in cell adhesion, trafficking, and signaling. Upon activation, integrins undergo dynamic conformational changes that regulate their affinity to ligands. The physiological functions and activation mechanisms of integrins have been heavily discussed in previous studies and reviews, but the fluorescence imaging techniques –which are powerful tools for biological studies– have not. Here we review the fluorescence labeling methods, imaging techniques, as well as Förster resonance energy transfer assays used to study integrin expression, localization, activation, and functions. More >

Kui Fu¹, Jiansheng Peng^{1, 2, *}, Hanxiao Zhang², Xiaoliang Wang³, Frank Jiang⁴

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1977-1997, 2020, DOI:10.32604/cmc.2020.09882

Abstract Single image super resolution (SISR) is an important research content in the field of computer vision and image processing. With the rapid development of deep neural networks, different image super-resolution models have emerged. Compared to some traditional SISR methods, deep learning-based methods can complete the superresolution tasks through a single image. In addition, compared with the SISR methods using traditional convolutional neural networks, SISR based on generative adversarial networks (GAN) has achieved the most advanced visual performance. In this review, we first explore the challenges faced by SISR and introduce some common datasets and evaluation metrics. Then, we review the… More >

Ming Zhao¹, Xinhong Liu¹, Xin Yao^{1, *}, Kun He²

CMC-Computers, Materials & Continua, Vol.64, No.3, pp. 1601-1614, 2020, DOI:10.32604/cmc.2020.09754

Abstract Although there has been a great breakthrough in the accuracy and speed of super-resolution (SR) reconstruction of a single image by using a convolutional neural network, an important problem remains unresolved: how to restore finer texture details during image super-resolution reconstruction? This paper proposes an Enhanced Laplacian Pyramid Generative Adversarial Network (ELSRGAN), based on the Laplacian pyramid to capture the high-frequency details of the image. By combining Laplacian pyramids and generative adversarial networks, progressive reconstruction of super-resolution images can be made, making model applications more flexible. In order to solve the problem of gradient disappearance, we introduce the Residual-in-Residual Dense… More >

Tingting Yang¹, Shuwen Jia¹, Hao Ma^{2, *}

CMC-Computers, Materials & Continua, Vol.62, No.3, pp. 1249-1258, 2020, DOI:10.32604/cmc.2020.05777

Abstract Underwater imaging is widely used in ocean, river and lake exploration, but it is affected by properties of water and the optics. In order to solve the lower-resolution underwater image formed by the influence of water and light, the image super-resolution reconstruction technique is applied to the underwater image processing. This paper addresses the problem of generating super-resolution underwater images by convolutional neural network framework technology. We research the degradation model of underwater images, and analyze the lower-resolution factors of underwater images in different situations, and compare different traditional super-resolution image reconstruction algorithms. We further show that the algorithm of… More >

Yanfen Guo^1,2, Zhe Cui^1,*, Zhipeng Yang³, Xi Wu², Shaahin Madani⁴

CMC-Computers, Materials & Continua, Vol.61, No.3, pp. 1205-1215, 2019, DOI:10.32604/cmc.2019.06029

Abstract Since the spatial resolution of diffusion weighted magnetic resonance imaging (DWI) is subject to scanning time and other constraints, its spatial resolution is relatively limited. In view of this, a new non-local DWI image super-resolution with joint information method was proposed to improve the spatial resolution. Based on the non-local strategy, we use the joint information of adjacent scan directions to implement a new weighting scheme. The quantitative and qualitative comparison of the datasets of synthesized DWI and real DWI show that this method can significantly improve the resolution of DWI. However, the algorithm ran slowly because of the joint… More >

Jiancheng Zou^1,*, Zhengzheng Li¹, Zhijun Guo¹, Don Hong²

CMC-Computers, Materials & Continua, Vol.60, No.1, pp. 163-177, 2019, DOI:10.32604/cmc.2019.05795

Abstract In the field of images and imaging, super-resolution (SR) reconstruction of images is a technique that converts one or more low-resolution (LR) images into a highresolution (HR) image. The classical two types of SR methods are mainly based on applying a single image or multiple images captured by a single camera. Microarray camera has the characteristics of small size, multi views, and the possibility of applying to portable devices. It has become a research hotspot in image processing. In this paper, we propose a SR reconstruction of images based on a microarray camera for sharpening and registration processing of array… More >