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一種基于反饋機制的閉環(huán)圖像去霧算法

馬時平,  李權(quán)合,  張晟翀

馬時平, 李權(quán)合, 張晟翀. 一種基于反饋機制的閉環(huán)圖像去霧算法[J]. 電子與信息學(xué)報, 2016, 38(2): 400-407. doi: 10.11999/JEIT150494
引用本文: 馬時平, 李權(quán)合, 張晟翀. 一種基于反饋機制的閉環(huán)圖像去霧算法[J]. 電子與信息學(xué)報, 2016, 38(2): 400-407. doi: 10.11999/JEIT150494
MA Shiping, LI Quanhe, ZHANG Shengchong. An Adaptive Closed-loop Image Dehazing Algorithm Based on the Feedback Mechanism[J]. Journal of Electronics & Information Technology, 2016, 38(2): 400-407. doi: 10.11999/JEIT150494
Citation: MA Shiping, LI Quanhe, ZHANG Shengchong. An Adaptive Closed-loop Image Dehazing Algorithm Based on the Feedback Mechanism[J]. Journal of Electronics & Information Technology, 2016, 38(2): 400-407. doi: 10.11999/JEIT150494

一種基于反饋機制的閉環(huán)圖像去霧算法

doi: 10.11999/JEIT150494
  • 1.

    (空軍工程大學(xué)航空航天工程學(xué)院 西安 710038) ②(光電信息控制和安全技術(shù)重點實驗室 三河 065201)

基金項目: 

國家自然科學(xué)基金(61372167, 61379104)

An Adaptive Closed-loop Image Dehazing Algorithm Based on the Feedback Mechanism

  • 1.

    (Aeronautics and Astronautics Engineering College, Air Force Engineering University, Xi'an 710038, China)

  • 2.

    (Optical Information Control and Security Technology Laboratory, Sanhe 065201, China)

Funds: 

The National Natural Science Foundation of China (61372167, 61379104)

  • 摘要: 針對目前去霧算法易受大氣環(huán)境隨機性和復(fù)雜性影響而造成自適應(yīng)性不強的問題，該文提出一種具有反饋機制的自適應(yīng)閉環(huán)去霧算法。該算法首先通過基于人眼視覺的特征認知評價進行參數(shù)初始化；然后利用去霧強度評價結(jié)果對反饋校正局部對比度參數(shù)進行調(diào)節(jié)，從而對去除加性光照后的圖像進行自適應(yīng)局部對比度提升；最后借鑒去霧后圖像的自然度設(shè)定迭代終止條件，決定是否輸出去霧結(jié)果。實驗表明該算法能夠自適應(yīng)提升不同退化類型、不同退化程度下的霧天圖像對比度，且去霧結(jié)果的信息熵和清晰度質(zhì)量評價指標優(yōu)于已有算法。
    Abstract: To solve the problem of low adaptability in existing dehazing algorithms caused by the randomness and complexity of atmospheric environment, an adaptive closed-loop dehazing algorithm based on the feedback mechanism is proposed. Firstly, parameters in the proposed algorithm are initialized according to human visual system based characteristic cognitive assessment. Secondly, the estimation of dehazing strength is given as the feedback to correct parameters of local contrast adjustment method, and then adaptively improve the local contrast of image after removing additive light. Finally, the terminating condition is set according to the naturalness of image after dehazing to determine whether to output the result. Experimental results show that the proposed algorithm can adaptively improve the contrast of hazy images with a variety of degradation types and degrees, and the evaluation of information entropy and definition of dehazing results is better than those of other existing algorithms.
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出版歷程
  • 收稿日期:  2015-04-29
  • 修回日期:  2015-10-23
  • 刊出日期:  2016-02-19

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