显著性检测指导的高光区域修复

目的为解决传统的基于光照模型的高光修复算法无法很好地对高光区域存在饱和现象的单幅图像进行处理这一问题,提出一种显著性检测指导的高光区域修复算法。方法首先在亮度空间应用显著性模型,实现高光区域的自动检测和标记,之后运用改进的Exemplar-Based算法,综合利用图像的邻域和边缘信息,对标记的高光区域进行自适应修复,去除图像中的高光。结果分别对仿真及自然场景下的高光图像进行测试,实验结果表明,与原修复算法和传统高光去除算法相比,所提算法的修复效果更符合人眼视觉、修复后的图像质量更好。结论本文算法与Exemplar-Based算法及Tan方法相比,对高光区域存在饱和现象的单幅图像有较好的修复效果,并且有效地克服了传统高光去除算法受光照模型限制的缺点。

Highlight area inpainting guided by saliency detection

Objective In order to deal with that most traditional highlight removal algorithms based on an illumination model fail to perform well on those images which have saturated pixels,this paper presents an inpainting algorithm guided by saliency detection. Method First,we apply the saliency model to the YUV space to detect and mark the highlight areas automatically. Then, we inpaint the highlight areas marked by the saliency model with the modified self-adaptive Exemplar-Based algorithm. Result We test on natural scene and emulational images,experimental results demonstrate that compared with the classic image inpainting and highlight removal algorithms,the results of the proposed method are more nature and have better image quality. Conclusion Compared with Exemplar-based and Tan algorithms,the proposed method performs better on dealing with a single image in which the highlight areas are saturated and is not limited by the illumination model.