PAR Model SAR Image Interpolation Algorithm on GPU with CUDA

Abstract

ABSTRACTResolution of synthetic aperture radar (SAR) images is limited by imaging hardware. To improve SAR image resolution, interpolation needs to be carried out on a SAR image. An image interpolation algorithm with a good effect incurs a high computational cost and cannot be used in a real-time scenario. This paper applies Compute Unified Device Architecture (CUDA) to speed up the piece-wise autoregressive model image interpolation algorithm for acquiring high-quality SAR images. A partial differential is used to carry out image de-noising. After de-noising, divide the image interpolation based on a local window of a piece-wise autoregressive model: the SAR image is first divided into many 9 × 9 small local windows. For each window, a CUDA thread is launched to interpolate using the autoregressive interpolation algorithm. Gradient descent algorithm is used to estimate parameters of the autoregressive model in the first- and second-round interpolation. Numerical simulation indicates that this GPU-based parallel algorithm can interpolate a 2592 × 1944 image within 1/110th of the time used by a CPU-based serial algorithm. Moreover, the computation time saved increases with the image size. The experimental results show that the method in this paper can achieve high-quality image interpolation in a low computation time.