Power – Efficient Gaussian Filter Architecture Utilizing Approximate 4:2 Compressors for Edge Detection
DOI:
https://doi.org/10.22399/ijcesen.4092Keywords:
Approximate Compressors, Gaussian Filter, Low Power, Image Processing, Edge DetectionAbstract
Nowadays, approximate computing is an emerging trend widely applied in image processing, signal processing, and computer vision applications. Approximation is suitable for applications which are error tolerant. In this work, approximate computing is used in the Gaussian filtering stage of edge detection. In computer vision applications like medical imaging, transportation etc., Edge detection plays a crucial role. In edge detection algorithms, the Gaussian filtering block is a fundamental module. This block involves power-consuming addition operations, resulting in increased computational complexity, power consumption, and delay. To address these trade-offs, imprecise computation is applied in the Gaussian filter module. The aim of the work is to reduce computational complexity, reduction in usage of power and delay in the Gaussian filtering module using an approximate compressor-based architecture. The proposed Gaussian filter is designed using newly developed approximate compressors. Simulation outcome indicates that the Gaussian filter implemented with proposed newly developed compressors achieves decline in power and delay compared to exact and existing implementations in literature. Error analysis and Edge Detection is performed using different metrics in MATLAB, and the results demonstrate good PSNR values for the proposed compressors, confirming their suitability for edge detection in image processing applications.
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