Analysis of Advanced 2D Convolution in Image Processing by Using AVX and OpenMP

In the field of digital image processing, the image data are required to operate for some enhancement operations such as image filtering, image restoration, image transformation, and so on. The memory allocation of image data is presented in an array and executed by two-dimensional matrix-matrix multiplication. The convolution operation is an essential operation among many filtering algorithms (e.g. Gaussian filter). In this paper, the characteristics of different convolution methods have been examined, and the important role of convolution and advanced parallel version of 2D convolution to improve the performance and instructions per clock cycle on a multi-core architecture by using AVX and OpenMP. Generally, convolution is performed by sliding the kernel over the image and moving the left side direction of the kernel through all data of the input matrix until all values of the image have been calculated. This paper intends to utilize and analyze how 1D, 2D, and 3D convolutions work in image processing and the parallel versions of 1D and 2D convolutions using padding on CPUs are proposed.