Resolution 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 denoising. 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.
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