基于双树复小波域的马尔可夫随机场样本修补算法

为了消除大目标图像修补过程中，修补区域由于累积误差引起的马赛克和振铃效应，提出基于双树复小波域的马尔可夫随机场(MRF)样本修补算法。首先应用双树复小波变换(DTCWT)将待修补图像变换到复频域，通过合理的置信度和数据项计算待修补块的修补顺序；然后应用MRF样本修补算法在不同尺度、不同方向下修补未知区域；最后利用双树复小波逆变换重构图像。实验结果表明，与传统离散小波修补方法相比，双树复小波域MRF样本修补算法能更好地保持修补区域纹理和结构信息。

MRF exemplar inpainting algorithm based on dual-tree complex wavelet domain

To eliminate the mosaic and "bell" effects due to cumulative errors during large object image inpainting,the Markov Random Fields(MRF) exemplar inpainting based on dual-tree complex wavelet domain was proposed.The image was converted to complex-frequency domain by Dual-Tree Complex Wavelet Transform(DTCWT) and the exemplar inpainting order was computed by rational confidence and data item,the unknown region was inpainted based on multiscale and multiband.The inpainted images were reconstructed by dual-tree complex wavelet inverse transform.The experimental results show that compared with classical discrete wavelet methods,the mosaic and "bell" effects can be avoided and the more favorable textural and structural information can be preserved.