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Image De-hazing Based on Polynomial Estimation and Steepest Descent Concept

机译:基于多项式估计和最陡血统概念的图像去沉降

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Digital images captured in hazy conditions suffer from colour distortion and loss of contrast, posing difficulties in being applied for further applications. Due to the existed challenge and its great significance, a large amount of research has been conducted for image de-hazing. Among the image haze removal methods, the algorithm based on dark channel prior is proved to be the most effective. Furthermore, the introduction of guided filter has boosted its efficiency to a large extent. However, the requirement for transmission refinement and the assumption that the transmission is the same in each colour channel still make the DCP concept based methods time consuming and suffer from colour distortion. To solve this problem, an approach named as Image De-hazing Based on Polynomial Estimation and Steepest Descent Concept (IDBPESDC) is proposed, which derives the pixel-wised transmission that does not require any further refinement. Additionally, image de-hazing procedures based on the steepest descent concept are adopted so that the objective of saturation enhancement under the minimum hue change constraint is achieved. Experiments are conducted on one hundred hazy images, processed by the proposed method and four other available approaches. Results are analysed qualitatively and quantitatively, which verified the effectiveness and efficiency of the proposed algorithm.
机译:在朦胧的条件下捕获的数字图像遭受彩色失真和对比度丧失,造成难以应用于进一步的应用。由于存在挑战和重要意义,已经进行了大量的研究,用于图像去沉肠。在图像雾霾去除方法中,证明基于暗信道的算法是最有效的。此外,引入引导过滤器在很大程度上推动了其效率。然而,对传输细化的要求和传输在每个颜色信道中的假设仍然使基于DCP概念的方法耗时并且遭受颜色失真。为了解决这个问题,提出了一种基于多项式估计和陡峭血迹概念(IDBPESDC)的作为图像去沉b的方法,该方法导出不需要任何进一步改进的像素设计的传输。另外,采用基于截最陡概念的图像去振荡过程,使得实现了在最小色调变化约束下的饱和增强的目的。实验在一百个朦胧的图像上进行,由所提出的方法和另外四种可用方法处理。定性和定量分析结果,验证了所提出的算法的有效性和效率。

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