Low light image enhancement methods based on classic Retinex model attempts to estimate the illumination component and to project it back to the corresponding reflectance. Therefore, the accuracy of illumination component estimated determines the performance of enhancement results. Based on Retinex model, this paper proposed an illumination component estimating method by guided filtering. In the proposed method, bright channel is used for obtaining a rough illumination map. Then, the gray image converted by the input low light image is employed as the guidance image and we refine the rough illumination map for obtaining a structural-awared illumination map by guided filtering. Lastly, the enhanced image can be achieved by a simple computation. Experimental results demonstrated that the performance of the proposed method significantly overpasses conventional low light image enhancement methods.
Aiming at the problems of image distortion and detail information loss occurring in recent dehazing algorithms, this paper proposes a dehazing algorithm based on dark channel prior with multi-scale weighted transmission fusion and self-adaptive gamma correction. Firstly, in order to refine the transmission map, a multi-scale weighted transmission fusion strategy with three scales is applied in the transmission estimation step. Then, a self-adaptive gamma correction method is proposed to enhance the contrast performance after applying the multi-scale weighted transmission fusion to image dehazing, and finally get the desired dehazed image. Experimental results demonstrated that the proposed algorithm can not only overcome the problems of image distortion and detail information loss well, but also yields a satisfied performance in comparison with tested similar methods.
The Euler number of a binary image is an important topological property for pattern recognition, image analysis, and computer vision. In the proposed algorithm, only three comparisons need to be completed for processing a bit-quad in the given image. Moreover, the proposed algorithm processes three rows simultaneously in the scanning which will reduce the number of checked pixels from 4 to 1.5 for processing each bit-quad, which will lead to an efficient processing. Experimental results demonstrated that the performance of the proposed algorithm significantly overpasses conventional Euler number computing algorithms.
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