一种基于反馈机制的闭环图像去雾算法

针对目前去雾算法易受大气环境随机性和复杂性影响而造成自适应性不强的问题，该文提出一种具有反馈机制的自适应闭环去雾算法。该算法首先通过基于人眼视觉的特征认知评价进行参数初始化；然后利用去雾强度评价结果对反馈校正局部对比度参数进行调节，从而对去除加性光照后的图像进行自适应局部对比度提升；最后借鉴去雾后图像的自然度设定迭代终止条件，决定是否输出去雾结果。实验表明该算法能够自适应提升不同退化类型、不同退化程度下的雾天图像对比度，且去雾结果的信息熵和清晰度质量评价指标优于已有算法。

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

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.