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Author:

Fang, Liu (Fang, Liu.) | Deng, ZhiRen (Deng, ZhiRen.)

Indexed by:

CPCI-S EI Scopus

Abstract:

Unmanned Aerial Vehicles (UAV) digital images are often badly degraded by noise during dynamic acquisition and transmission process. Denoising is very important and difficult for UAV-vision Guided, because natural scene image is complicated and having lots of the edges and texture details. The image denoising and enhancement algorithm based on adaptive dual-tree discrete wavelet packets(ADDWP) which combine the dual-tree discrete wavelet packets and evolutionary programming is proposed in this paper. In order to reduce the noise and enhance detail for a UAV image by the EP(evolutionary programming) in wavelet domain(.) Firstly, The de-noising threshold is estimated by EP in the wavelet domain. Secondly, Noise is reduced in the fine high-frequency sub-bands of each decomposition level, respectively, so that the maximum signal-noise ratio can be obtained in the high-frequency subbands, respectively. Thirdly, The enhancement parameters is estimated by EP in the wavelet domain, and the detail is adaptive enhanced by an improved non-linear gain operator in the coarse high-frequency subbands of each decomposition level, respectively. The experiment result improves our algorithm is efficient.

Keyword:

evolutionary programming dual-tree discrete wavelet packets image enhancing image denoising

Author Community:

  • [ 1 ] [Fang, Liu]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing, Peoples R China
  • [ 2 ] [Deng, ZhiRen]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing, Peoples R China

Reprint Author's Address:

  • [Fang, Liu]Beijing Univ Technol, Sch Elect Informat & Control Engn, Beijing, Peoples R China

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Source :

2013 SIXTH INTERNATIONAL SYMPOSIUM ON COMPUTATIONAL INTELLIGENCE AND DESIGN (ISCID), VOL 1

ISSN: 2165-1701

Year: 2013

Page: 47-50

Language: English

Cited Count:

WoS CC Cited Count: 0

SCOPUS Cited Count:

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 7

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