基于并行运算的双层图像锐化方法

针对低清晰度照片或图像放大后边界模糊、画质差及人们对高清图像的实际需求，基于统一计算架构(CUDA)环境，提出了一个两层结构的图像并行锐化方法，设计并实现了一个基于GPU的并行锐化算法：第一层采用并行线性插值法，反复对图像非边界部分进行计算以及边缘区域锐化处理；第二层采用改进的梯度法对图像进一步优化。放大后的图像经该方法处理后，基本上可消除图像边缘区域的锯齿，使图像画质平滑、自然、清晰。经实验验证，设计的基于GPU的并行锐化算法在效率和画质上都优于目前流行的算法，提出的方法可应用于现有图像及照片放大后处理。

Bi-level image sharpening method based on parallel computing

A parallel bi-level image sharpening methodology in Compute Unified Device Architecture (CUDA) circumstance was proposed especially for the improvements on fuzzy boundaries and poor quality when enlarging low resolution photos or images. A GPU-based parallel sharpening algorithm with two stages was designed and implemented. Firstly, the parallel linear interpolation algorism was repeatedly adopted by the calculation of non-edge region and the sharpening treatments of edge area. Secondly, an improved gradient method was utilized for the further optimized images. The jagged edges of the enlarged images were basically eliminated by the proposed method, making the images much more smooth, natural, and legible. The experimental results prove that the GPU-based parallel image sharpening algorithm is superior to the currently popular algorithms in calculation efficiency and image quality, and it can be applied in sharpening images and amplifying photos.