Open AccessSolder joint image adaptive block compressive sensing with convex optimisation and Gini index
Author(s) -
Zhao Huihuang,
Wang Yaonan,
Zheng Jinhua,
Qiao Zhijun,
Zhang Yun
Publication year - 2019
Publication title -
the journal of engineering
Language(s) - English
Resource type - Journals
ISSN - 2051-3305
DOI - 10.1049/joe.2018.9148
Subject(s) - robustness (evolution) , image compression , block (permutation group theory) , compressed sensing , algorithm , regular polygon , computer science , image (mathematics) , convex optimization , norm (philosophy) , mathematics , artificial intelligence , mathematical optimization , image processing , biochemistry , chemistry , geometry , political science , law , gene
This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient‐based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l 1 in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness.