Image blending techniques based on GPU acceleration

Abstract

Today, image blending has been used for a high-resolution image in medical, aerospace, and even defense areas. To blend images, several filters and various processing steps such as Gaussian pyramid, Laplacian pyramid, and multi-band computation will be needed. However, these computations consist of a large amount of arithmetic operations. As the processing capability of graphic processing units (GPUs) grows very rapidly, GPUs have commonly been used to supplement central processing units (CPUs) for high-performance computing. By employing hardware accelerators such as GPU, a significant speedup can be achieved. In this paper, we present an implementation of fast image blending methods using compute unified device architecture (CUDA). The proposed implementation utilizes a shared memory in GPU better than conventional implementations leading to a better speed-up. The proposed implementation of this paper shows an improvement of 3.9 times in the overall execution time compared to a conventional implementation.