Abstract
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.
Highlights
PLOS ONE | DOI:10.1371/journal.pone.0166056 November 10, 2016suppressing unwanted noise, detecting edges, image fusion, compression, multi scale decomposition etc. [4,5,6,7,8]
In this work we present a structurally optimized filter design along with an additional aptitude of versatility
19] which though reduces the multipliers count but narrow down the scope of design for implementing limited filter types, the proposed framework reduces the multipliers count and at the same time is capable of incorporating diverse range of filter types
Summary
PLOS ONE | DOI:10.1371/journal.pone.0166056 November 10, 2016suppressing unwanted noise, detecting edges, image fusion, compression, multi scale decomposition etc. [4,5,6,7,8]. The quadratic growth of n and M factors imply that the overall filtering operation is computationally expensive [11]. The implementation of these filters can either be carried out on a software or hardware. Hardware platforms such as Application Specific Integrated Circuits (ASIC) and Field Programmable Gate Arrays (FPGA) are high performance and fast. They satisfy the real time image processing requirements and received a great deal of attention for implementing filters in these areas [12, 13]
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