Correlation and convolution for binary image filter using QCA

Quantum-dot cellular automata (QCA) are an optimistic alternative to complementary metal–oxide–semiconductor technology (CMOS). QCA provide not only a dominant platform but also a well-organised computing platform for image processing, which possesses intense computational provisions. Convolution and correlation are the fundamentals of an image processing technique which plays a significant role in applications of image filtering in the spatial domain. This paper proposes proficient low-power nanoscale architecture to carry out convolution and correlation as binary image filters. This design using QCA technology is achieved for the first time. The results are evaluated against theoretical values, which provide functional accuracy for the proposed design. The inherent characteristics of QCA are exploited in order to design and implement the proposed circuit at a low-power nanoscale level. Matlab software is used to obtain the pixel intensity values of an image, and then these data are used as an input to the proposed circuit.