<span>Human pupil eye detection is a significant stage in iris segmentation which is representing one of the most important steps in iris recognition. In this paper, we present a new method of highly accurate pupil detection. This method is consisting of many steps to detect the boundary of the pupil. First, the read eye image (R, G, B), then determine the work area which is consist of many steps to detect the boundary of the pupil. The determination of the work area contains many circles which are larger than pupil region. The work area is necessary to determine pupil region and neighborhood regions afterward the difference in color and intensity between pupil region and surrounding area is utilized, where the pupil region has color and intensity less than surrounding area. After the process of detecting pupil region many steps on the resulting image is applied in order to concentrate the pupil region and delete the others regions by using many methods such as dilation, erosion, canny filter, circle hough transforms to detect pupil region as well as apply optimization to choose the best circle that represents the pupil area. The proposed method is applied for images from palacky university, it achieves to 100 % accurac</span>

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Haze causes the degradation of image quality. Thus, the quality of the haze must be estimated. In this paper, we introduce a new method for measuring the quality of haze images using a no-reference scale depending on color saturation. We calculate the probability for a saturation component. This work also includes a subjective study for measuring image quality using human perception. The proposed method is compared with other methods as, entropy, Naturalness Image Quality Evaluator (NIQE), Haze Distribution Map based Haze Assessment (HDMHA), and no reference image quality assessment by using Transmission Component Estimation (TCE). This done by calculating the correlation coefficient between non-reference measures and subjective measure, the results show that the proposed method has a high correlation coefficient values for Pearson correlation coefficient (0.8923), Kendall (0.7170), and Spearman correlation coefficient (0.8960). The image database used in this work consists of 70 hazy images captured by using a special device, design to capture haze image. The experiment on haze database is consistent with the subjective experiment.

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