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A Novel Parallel Architecture for Local Histogram Equalization

机译:用于局部直方图均衡的新型并行架构

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Local histogram equalization is an image enhancement algorithm that has found wide application in the pre-processing stage of areas such as computer vision, pattern recognition and medical imaging. The computationally intensive nature of the procedure, however, is a main limitation when real time interactive applications are in question. This work explores the possibility of performing parallel local histogram equalization, using an array of special purpose elementary processors, through an HDL implementation that targets FPGA or ASIC platforms. A novel parallelization scheme is presented and the corresponding architecture is derived. The algorithm is reduced to pixel-level operations. Processing elements are assigned image blocks, to maintain a reasonable performance-cost ratio. To further simplify both processor and memory organizations, a bit-serial access scheme is used. A brief performance assessment is provided to illustrate and quantify the merit of the approach.
机译:局部直方图均衡化是一种图像增强算法,已广泛应用于计算机视觉,模式识别和医学成像等领域的预处理阶段。但是,当对实时交互式应用程序提出疑问时,该过程的计算量大的性质是主要限制。这项工作探索了通过针对FPGA或ASIC平台的HDL实现,使用一系列特殊目的基本处理器来执行并行局部直方图均衡的可能性。提出了一种新颖的并行化方案,并推导了相应的体系结构。该算法被简化为像素级操作。为处理元素分配了图像块,以保持合理的性能成本比。为了进一步简化处理器和内存的组织,使用了位串行访问方案。提供简短的性能评估以说明和量化该方法的优点。

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