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Design of an Efficient Architecture for Enhancement of Stream Video Captured in Non-uniform Lighting Conditions

机译:在非均匀照明条件下捕获的流视频增强的高效架构设计

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A novel architecture for performing hue-saturation-value (HSV) domain enhancement of digital color images with non-uniform lighting conditions is proposed in this paper for video streaming applications. The approach promotes log-domain computation to eliminate all multiplications, divisions and exponentiations utilizing the effective logarithmic estimation modules. An optimized quadrant symmetric architecture is incorporated into the design of homomorphic filter for the enhancement of intensity value. Efficient modules are also presented for conversion between RGB and HSV color spaces. The design is able to bring out details hidden in shadow regions of the image. It is capable of producing 187.86 million outputs per second (MOPs) on Xilinx''s Virtex II XC2V2000-4ff896 field programmable gate array (FPGA) at a clock frequency of 187.86 MHz. It can process over 179.1 (1024 脳 1024) frames per second and consumes approximately 70.7% and 76.8% less hardware resource with 127% and 280% performance boost when compared to the designs with machine learning algorithm 1101, and with separated dynamic and contrast enhancements l11l, respectively.
机译:在本文中提出了一种用于执行具有非均匀照明条件的Hue饱和值(HSV)域增强的新型架构,用于视频流应用。该方法促进了利用有效对数估计模块的所有乘法,划分和指数来消除日志域计算。优化的象限对称架构被纳入均匀滤波器的设计中,以提高强度值。还提供高效模块来转换RGB和HSV颜色空间。该设计能够在图像的阴影区域中介绍隐藏的详细信息。它能够在Xilinx的Virtex II XC2V2000-4FF896现场可编程门阵列(FPGA)上产生187.86亿秒输出(MOP),以187.86 MHz的时钟频率。它可以在每秒超过179.1(1024÷1024)帧,并在与具有机器学习算法1101的设计相比,使用127%和280%的性能提升时,每秒减少约70.7%和76.8%,并且具有分离的动态和对比度增强L11L分别。

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