Rain Removal in Video by Spatial-Temporal Bilateral Filter
Date Issued
2012
Date
2012
Author(s)
Wang, Po-Hsiang
Abstract
Abstract
As outdoor surveillance systems have been widely deployed everywhere, the emphasis on the quality of their frames also rises simultaneously. Rain is a weather phenomenon we frequently encounter in our daily lives, and it is also one the most severe interferences to the frames captured in outdoor surveillance systems, which usually leads to unclear frames and makes users hard to judge and analyze the image information. It also causes the failures of several automatic video analysis subsystems in video surveillance, such as feature detection, segmentation, and object recognition. Due to the random and swift movement of raindrops, it becomes a challenge to remove them from the images and to restore the noisy images back to the clear one. Several previous works have been proposed in literatures; however, most of them can only deal with limited conditions, and several important parameters are required to be set manually, which makes these approaches not feasible for surveillance systems.
In this thesis, since the rain drops result in complex space-and-time-varying signals in the images, we treat the rain removal problem as am image de-noising problem. We first propose to employ spatial-temporal bilateral filter as a raindrop filter. Moreover, we also propose software and hardware cooperation methods along with the function of adaptive parameters to achieve the “plug and play” capability required for easy deployment in real applications. The experimental results show that the efficiency of this algorithm surpasses those of other existing algorithms.
Detailed hardware architecture analysis is also presented in this thesis to derive an efficient hardware architecture for the proposed algorithm. We also implement the hardware as a chip by using the ASIC design flow with TSMC 90nm technology, and the die size is 2.352 x 2.354mm². Under the working frequency of 125 MHz, the chip can support real-time 30 1920x1080 fps video processing capability.
Subjects
surveillance systems
de-noising
raindrop filter
Type
thesis
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