|Alternative Title||Color Image Defogging and Defogged Image Quality Assessment for Detection and Recognition|
|Keyword||图像去雾 图像增强 天空识别 去雾图像评价 颜色对比度描述子|
|Place of Conferral||沈阳|
Aiming at the demand of image defogging and enhancement tasks for detection and recognition, after analyzing atmospheric scattering theory and foggy image degradation mechanism, this paper summarizes the typical methods in the field of image defogging and defogged image quality assessment, and carries out deep research on several key issues that were not effectively solved in this field. This paper mainly focuses on four research contents: accurate estimation of sky light, scene adaptive defogging, defogged image enhancement and defogging effect assessment. The main research works are summarized as follows: （1） Method of quantitative assessment on image defogging effect. During the assessment task of color image defogging effect for detection and recognition, the improvement effect of contrast is taken as the main focus target, and a color contrast descriptor of defogged image based on the haze-line theory is proposed. Aiming at the problem that the foggy-free image of the same scene cannot be obtained as the evaluation reference during the process of image defogging effect assessment, based on the degradation mechanism of foggy image and the haze-line theory, this paper clusters the pixels in foggy image, and measures the local color contrast of restored image though computing the inter-cluster standard deviation of different color clusters within local image patch. Both subjective evaluation experiments and synthetic image evaluation experiments are designed and carried out, the experimental results verify the scientificity of the proposed evaluation indicator. （2） Method of scene adaptive image defogging. In order to improve the adaptive ability of image defogging algorithm to scene and achieve better automatic defogging effect in different scenes, this paper proposes an improved image defogging method based on the dark channel prior, namely scene adaptive dark channel defogging method. The method adjusts the scale of dark channel according to the color and edge features of image, and takes into account the advantages of defogging results achieved by different scales. The method achieves significant enhancement in contrast, natural restoration in color and great suppression in “halo” effect. Furthermore, in order to solve the problem of low brightness and color distortion caused by unreasonable sky light estimation, the constraints of sky light estimation are proposed from the physical meaning of sky light, which provide a basis for more accurate sky light estimation. （3） Method of sky detection in foggy image. A sky detection method for foggy scene is proposed in order to obtain a more accurate estimation of sky light according with its physical meaning. The method introduces several fog-relevant features that reflect the foggy perceptual density and the scene depth to characterize sky. Based on these features, the sky with arbitrary shape is detected by Imbalanced-SVM classifying and similarity measuring. Moreover, a sky dataset of foggy scene HazySky is built for model training and performance evaluation. To evaluate the performance of the proposed sky detection method, extensive experiments are conducted both on the HazySky dataset and the SkyFinder dataset. The experimental results demonstrate that the proposed method has strong adaptability to weather and lighting conditions, and achieves higher detection accuracy not only in foggy images but also in images of other weather conditions. （4） Method of image defogging and enhancement by effective sky detection. In order to improve the definition and contrast of foggy-degraded images, a defogging and enhancement method based on effective sky detection is proposed. This method, firstly, can improve the accuracy of sky light estimation by applying sky detection method to the sky light estimation step of image defogging process, secondly, can reduce "halo" effect of restored image by applying scale adaptive dark channel method to the transmission estimation step of image defogging process, and finally, can further improve the contrast and visual effect of restored images by applying adaptive gamma correction method to the image enhancement step after image defogging process. The experimental results demonstrate that the entropy and color contrast of the images processed by the proposed method are greatly improved than ones without enhancing after defogging, and the defogging process does not cause obvious structural distortion.
|宋颖超. 面向探测识别的彩色图像去雾与评价方法研究[D]. 沈阳. 中国科学院沈阳自动化研究所,2018.|
|Files in This Item:|
|面向探测识别的彩色图像去雾与评价方法研究（21405KB）||学位论文||开放获取||CC BY-NC-SA||Application Full Text|
|Recommend this item|
|Export to Endnote|
|Similar articles in Google Scholar|
|Similar articles in Baidu academic|
|Similar articles in Bing Scholar|
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.