Yibin He^1,2, Wenhao Xiong¹, Hanxin Chen^1,2,*, Yuchen Chen¹, Qiaosen Dai¹, Panpan Tu¹, Gaorui Hu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.126, No.1, pp. 379-396, 2021, DOI:10.32604/cmes.2021.010771 - 22 December 2020

Abstract The acquisition of images with a fish-eye lens can cause serious image distortion because of the short focal length of the lens. As a result, it is difficult to use the obtained image information. To make use of the effective information in the image, these distorted images must first be corrected into the perspective of projection images in accordance with the human eye’s observation abilities. To solve this problem, this study presents an adaptive classification fitting method for fish-eye image correction. The degree of distortion in the image is represented by the difference value of More >

Feng Yuan, Xiao Shao^*

Journal on Big Data, Vol.2, No.4, pp. 167-176, 2020, DOI:10.32604/jbd.2020.015357 - 24 December 2020

Abstract Traditional image quality assessment methods use the hand-crafted features to predict the image quality score, which cannot perform well in many scenes. Since deep learning promotes the development of many computer vision tasks, many IQA methods start to utilize the deep convolutional neural networks (CNN) for IQA task. In this paper, a CNN-based multi-scale blind image quality predictor is proposed to extract more effectivity multi-scale distortion features through the pyramidal convolution, which consists of two tasks: A distortion recognition task and a quality regression task. For the first task, image distortion type is obtained by More >

Yao Ma¹, Xibiao Cai^{1, *}, Fuming Sun²

CMES-Computer Modeling in Engineering & Sciences, Vol.123, No.1, pp. 201-216, 2020, DOI:10.32604/cmes.2020.07867 - 01 April 2020

Abstract Image quality assessment has become increasingly important in image quality monitoring and reliability assuring of image processing systems. Most of the existing no-reference image quality assessment methods mainly exploit the global information of image while ignoring vital local information. Actually, the introduced distortion depends on a slight difference in details between the distorted image and the non-distorted reference image. In light of this, we propose a no-reference image quality assessment method based on a multi-scale convolutional neural network, which integrates both global information and local information of an image. We first adopt the image pyramid More >