一种双约束稀疏模型图像修复算法

针对图像处理中需要修复大面积缺损区域的问题,提出一种基于双约束稀疏模型的图像修复算法.该方法首先在已知区域内搜索与待填充目标块相似的样本,将每个样本块都视为一个高维向量,则相似的样本在高维空间中都在目标块的邻域内.假设邻域中的样本处于同一流形上,使用局部线性嵌入方法对未知区域进行估计,然后利用稀疏表示模型得到最终结果.实验结果表明,与传统的基于样本块的修复方法相比较,使用该算法修复后的图像纹理和结构信息更加清晰.

An Image Inpainting Algorithm Based on Sparse Modeling with Double Constraints

An exemplar-based inpainting algorithm via sparse representation is presented to focus on the problem of removing large objects from digital image.Exemplars that are similar with the target patch are searched in known regions.These exemplars are all neighbors of the target patch in a high-dimensional data space by regarding each of them as a high-dimensional vector.The unknown region is estimated through locally linear embedding method by supposing these neighbors in a same manifold,and then,sparse representation is applied to enforce compatibility and sharpness with the surrounding region.Experimental results and comparison with the traditional exemplar-based inpainting algorithm show that the proposed algorithm can effectively repair the texture and structure information in the damaged image.