In this paper, an adaptive method is proposed to address the problems of over-enhancement of weak light image enhancing and adaptivity of parameter settings based on local gamma transform and illumination-reflection model. In which the source image is converted from RGB color space into the YUV color space firstly, and the illumination distribution of the scene is extracted by using a guided filtering function. Then, an adaptive local gamma transform is designed to perform enhancement on the illumination component and the dynamic range of gray-scale is expanded. Finally, the image is changed from YUV space to RGB space. Experimental results shows that the proposed algorithm can not only effectively improve the visual effect of the uneven light image but also reveal more detailed information in dark regions.
Particles adhered together will influence the image analysis in computer vision system. In this paper, a method based on concave point is designed. First, corner detection algorithm is adopted to obtain a rough estimation of potential concave points after image segmentation. Then, it computes the area ratio of the candidates to accurately localize the final separation points. Finally, it uses the separation points of each particle and the neighboring pixels to estimate the original particles before adhesion and provides estimated profile images. The experimental results have shown that this approach can provide good results that match the human visual cognitive mechanism.
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