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A Low Cost Structurally Optimized Design for Diverse Filter Types

机译：适用于多种滤波器类型的低成本结构优化设计

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摘要

A wide range of image processing applications deploys two dimensional (2D)-filters for performing diversified tasks such as image enhancement, edge detection, noise suppression, multi scale decomposition and compression etc. All of these tasks require multiple type of 2D-filters simultaneously to acquire the desired results. The resource hungry conventional approach is not a viable option for implementing these computationally intensive 2D-filters especially in a resource constraint environment. Thus it calls for optimized solutions. Mostly the optimization of these filters are based on exploiting structural properties. A common shortcoming of all previously reported optimized approaches is their restricted applicability only for a specific filter type. These narrow scoped solutions completely disregard the versatility attribute of advanced image processing applications and in turn offset their effectiveness while implementing a complete application. This paper presents an efficient framework which exploits the structural properties of 2D-filters for effectually reducing its computational cost along with an added advantage of versatility for supporting diverse filter types. A composite symmetric filter structure is introduced which exploits the identities of quadrant and circular T-symmetries in two distinct filter regions simultaneously. These T-symmetries effectually reduce the number of filter coefficients and consequently its multipliers count. The proposed framework at the same time empowers this composite filter structure with additional capabilities of realizing all of its Ψ-symmetry based subtypes and also its special asymmetric filters case. The two-fold optimized framework thus reduces filter computational cost up to >75% as compared to the conventional approach as well as its versatility attribute not only supports diverse filter types but also offers further cost reduction via resource sharing for sequential implementation of diversified image processing applications especially in a constraint environment.
机译：各种各样的图像处理应用程序都部署了二维（2D）滤波器来执行各种任务，例如图像增强，边缘检测，噪声抑制，多尺度分解和压缩等。所有这些任务都需要同时使用多种2D滤波器来获得预期的结果。尤其是在资源约束环境中，资源匮乏的常规方法对于实现这些计算密集型2D滤波器不是可行的选择。因此，它需要优化的解决方案。通常，这些过滤器的优化基于利用结构特性。所有先前报告的优化方法的共同缺点是它们仅适用于特定过滤器类型，其适用范围有限。这些范围狭窄的解决方案完全忽略了高级图像处理应用程序的多功能性，从而抵消了它们在实现完整应用程序时的有效性。本文提出了一个有效的框架，该框架利用2D滤波器的结构特性来有效地降低2D滤波器的计算成本，并具有支持多种滤波器类型的多功能性的额外优势。介绍了一种复合对称滤波器结构，该结构同时利用两个不同滤波器区域中的象限和圆形T对称性。这些T对称有效地减少了滤波器系数的数量，因此减少了其乘数。同时，所提出的框架使该复合滤波器结构具有实现其所有基于Ψ对称性的子类型以及其特殊非对称滤波器情况的附加功能。因此，与传统方法相比，经过双重优化的框架将过滤器的计算成本降低了> 75 ，而且其多功能性不仅支持多种过滤器类型，而且还通过资源共享进一步降低了成本顺序执行各种图像处理应用程序，尤其是在约束环境中。

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