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Open AccessArticle

An ISAR and Visible Image Fusion Algorithm Based on Adaptive Guided Multi-Layer Side Window Box Filter Decomposition

by Jiajia Zhang, Huan Li, Dong Zhao, Pattathal V. Arun, Wei Tan, Pei Xiang, Huixin Zhou, Jianling Hu and Juan Du

Remote Sens. 2023, 15(11), 2784; https://doi.org/10.3390/rs15112784 - 26 May 2023

Cited by 1 | Viewed by 2443

Abstract

Traditional image fusion techniques generally use symmetrical methods to extract features from different sources of images. However, these conventional approaches do not resolve the information domain discrepancy from multiple sources, resulting in the incompleteness of fusion. To solve the problem, we propose an asymmetric decomposition method. Firstly, an information abundance discrimination method is used to sort images into detailed and coarse categories. Then, different decomposition methods are proposed to extract features at different scales. Next, different fusion strategies are adopted for different scale features, including sum fusion, variance-based transformation, integrated fusion, and energy-based fusion. Finally, the fusion result is obtained through summation, retaining vital features from both images. Eight fusion metrics and two datasets containing registered visible, ISAR, and infrared images were adopted to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed asymmetric decomposition method could preserve more details than the symmetric one, and performed better in both objective and subjective evaluations compared with the fifteen state-of-the-art fusion methods. These findings can inspire researchers to consider a new asymmetric fusion framework that can adapt to the differences in information richness of the images, and promote the development of fusion technology. Full article

(This article belongs to the Special Issue Advances and Challenges on Multisource Remote Sensing Image Fusion: Datasets, New Technologies, and Applications)

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21 pages, 2065 KB

Open AccessArticle

Fabric Defect Detection Based on Illumination Correction and Visual Salient Features

by Lan Di, Hanbin Long and Jiuzhen Liang

Sensors 2020, 20(18), 5147; https://doi.org/10.3390/s20185147 - 9 Sep 2020

Cited by 14 | Viewed by 12630

Abstract

Aiming at the influence of uneven illumination on fabric feature extraction and the limitations of traditional frequency-based visual saliency algorithms, we propose a fabric defect detection method based on the combination of illumination correction and visual salient features—(1) Construct a multi-scale side window box (MS-BOX) filter to extract the illumination component of the image, then use the constructed two-dimensional gamma correction function to perform illumination correction on the image in the global angle, and finally enhance the local contrast of the image in the local angle; (2) Use the

L_{0}

gradient minimization method to remove the background texture of fabric images and highlight the defects; (3) Represent the fabric image as a quaternion image, where each pixel in the image is represented by a quaternion consisting of color, intensity and edge characteristics. The two-dimensional fractional Fourier transform (2D-FRFT) is used to obtain the saliency map of the quaternion image. Experiments show that our method has a higher overall recall rate for defect detection of star-patterned, box-patterned, and dot-patterned fabrics, and the overall recall-precision effect is better than other existing methods. Full article

(This article belongs to the Section Sensing and Imaging)

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