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基于重加权数据项的边缘保持图像平滑算法

Edge Preserving Image Smoothing Algorithm Based on Reweight Data Items

  • 摘要: 为避免弱梯度边缘信息被平滑、噪声纹理信息因具有和结构像素相似的强梯度特点而被保留的问题, 提出一种优化全局算法的框架. 该方法将数据项拆分为两项, 通过计算图像中当前像素与局部邻域像素间的相似性初步判断待处理像素是否位于结构区域, 并据此设计分项后的两个权重来分别约束待处理像素与原图相似或与滤波结果相似, 继而实现结构信息的保留和非结构信息的平滑. 依靠其强大的灵活性, 该框架适用于大部分全局优化算法. 最后将该框架应用在基于L2、L0和L1的平滑算法实例中与其他算法进行综合对比实验, 并使用PSNR和SSIM评价指标在BSD300数据集上对各个算法进行评估, 验证了所提框架的实用性, 且改进后的算法不仅能保持弱梯度结构信息, 还能产生优于原始算法的图像平滑效果.

     

    Abstract: In order to avoid the problem that the weak gradient edge information is smoothed and the noise texture information is retained due to the strong gradient characteristics similar to the structure pixels, a global optimization algorithm framework is proposed. By calculating the similarity between the current pixel in the image and the pixel in the local neighborhood, the method initially determines whether the pixel to be processes is located in the structure area. Based on this, the two weights after the sub-item are designed to constrain the pixel to be processed to be similar to the original image or similar to the filtering result, so as to realize the retention of structural information and the smoothing of unstructured information. Due to its strong flexibility, the framwork is suitable for most global optimization algorithms. Finally, the framework is applied to the smoothing algorithm example based on L2, L0, L1, and to conduct comprehensive comparison experiments with other algorithms,   and PSNR and SSIM evaluation indexes are used to evaluate each algorithm on the BSD300 data set, which verifies the practicality of the proposed framework, and the improved algorithm can not only keep the weak gradient structure information, but also maintain the structure information. The image smoothing effect is better than the original algorithm.

     

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