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author:

Weng, Daochen (Weng, Daochen.) [1] | Zheng, Qianying (Zheng, Qianying.) [2] | Yang, Bingkun (Yang, Bingkun.) [3]

Indexed by:

EI

Abstract:

The fusion algorithm of traditional image stitching does not fully consider the differences of the clarity of the two images, and the conventional Discrete Wavelet Transform algorithm would blur the image when applied to image stitching. Owing to these, an improved method based on Discrete Wavelet Transform and Slope Fusion is proposed. The proposed algorithm firstly performs Haar wavelet transform on the image to be fused to obtain a low-frequency component and multiple high-frequency components. Subsequently, the Slope Fusion method is used for the obtained low-frequency component and the sub-regional Slope Fusion method is used for the high-frequency components. Finally, the fused image is obtained by using the Inverse Discrete Wavelet Transform for the new low-frequency component and high-frequency components. The proposed algorithm can retain the information of direction and detail while taking full account of differences in image sharpness, all of those benefits help improve the quality of the fused image effectively. The experimental results show that the proposed algorithm can make the fused image clearer and objectively enhance multiple fusion indicators of the fused image. © Springer Nature Switzerland AG 2019.

Keyword:

Artificial intelligence Discrete wavelet transforms Image compression Image enhancement Image fusion Inverse transforms Signal reconstruction

Community:

  • [ 1 ] [Weng, Daochen]College of Physics and Information Engineering, Fuzhou University, Fuzhou; 350116, China
  • [ 2 ] [Zheng, Qianying]College of Physics and Information Engineering, Fuzhou University, Fuzhou; 350116, China
  • [ 3 ] [Yang, Bingkun]College of Physics and Information Engineering, Fuzhou University, Fuzhou; 350116, China

Reprint 's Address:

  • [zheng, qianying]college of physics and information engineering, fuzhou university, fuzhou; 350116, china

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Source :

ISSN: 0302-9743

Year: 2019

Volume: 11909 LNAI

Page: 144-155

Language: English

0 . 4 0 2

JCR@2005

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 1

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 1

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