Hazes with various properties spread widely across flat areas with depth continuities and corner areas with depth discontinuities. Removing haze from a single hazy image is difficult due to its ill-posed nature. To solve this problem, this study proposes a modified hybrid median filter that performs a median filter to preserve the edges of flat areas and a hybrid median filter to preserve depth discontinuity corners. Recovered scene radiance, which is obtained by removing hazy particles, restores image visibility using adaptive nonlinear curves for dynamic range expansion. Using comparative studies and quantitative evaluations, this study shows that the proposed method achieves similar or better results than those of other state-of-the-art methods.

This paper proposes a foreground segmentation method for indoor environments using depth images only. It uses a morphological operator and histogram analysis to segment the foreground. In order to compare the accuracy for foreground segmentation, we use metric measurements of false positive rate (FPR), false negative rate (FNR), total error (TE), and a similarity measure (S). A series of experimental results using video sequences collected under various circumstances are discussed. The proposed system is also designed in a field-programmable gate array (FPGA) implementation with low hardware resources.