基于视觉注意模型和粒子群算法的图像插值

Hsuan-Ying Chen, Jin-Jang Leou
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引用次数: 2

摘要

本文提出了一种基于视觉注意模型和粒子群优化(PSO)的图像边缘插值方法。首先,本文提出的视觉注意模型有效地生成了待插值图像的高质量显著性图。然后,在显著性映射的基础上,对非显著性块(non- roi)和显著性块(roi)分别采用双线性插值和所提出的粒子群插值,得到最终的插值结果。该方法适用于任意放大倍数的图像插值。实验结果表明,本文方法的插值结果优于三种对比方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Image interpolation using visual attention model and particle swarm optimization
In this study, a new edge-directed image interpolation approach using visual attention model and particle swarm optimization (PSO) is proposed. First, a high-quality saliency map of an image to be interpolated is generated by the proposed visual attention model in an effective manner. Then, based on the saliency map, bilinear interpolation and the proposed PSO interpolation are employed for non-saliency blocks (non-ROIs) and saliency blocks (ROIs), respectively, to obtain the final interpolation results. The proposed approach is applicable for image interpolation with arbitrary magnification factors (MFs). Based on the experimental results obtained in this study, the interpolation results of the proposed approach are better than those of three comparison methods.
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