破损图像丢失区域自适应优化修复方法仿真  

Simulation of Adaptive Optimization and Repair Method for Damaged Image Loss Region

作　　者：廖晓芳[1] LIAO Xiao-fang(South China Business College,GDUFS,Guangzhou Guangdong,510545,China)

机构地区：[1]广东外语外贸大学南国商学院

出　　处：《计算机仿真》2019年第6期388-392,共5页Computer Simulation

基　　金：广州市哲学社会科学发展“十三五”(2016GZQN23)

摘　　要：针对传统修复方法存在图像模糊、修复速度低的问题,提出一种基于纹理合成的破损图像丢失区域自适应修复方法。对破损图像丢失区域进行纹理分割提取破损图像边缘能量特征,完成自适应纹理密度量化估计,计算丢失区域纹理向量量化区域的超像素级视觉特征。利用基于Criminisi算法根据纹理特性计算优先修复区域,不断更新图像的可靠度对丢失区域进行纹理修复,完成破损图像丢失区域的自适应修复。通过实验结果验证,所提修复方法与传统修复方法相比,修复后的图像更清晰、完整,修复用时更短。In traditional restoration methods,the image is fuzzy and restoration speed is low.Therefore,an adaptive restoration method for lost region in damaged image based on texture synthesis is proposed.At first,the energy feature of damaged image edge was extracted by texture segmentation of lost region in damaged image.Then,the adaptive texture density quantization estimation was completed.The texture vector of lost area was calculated and super-pixel visual feature was quantized.Based on Criminisi algorithm,priority restoration region was computed through the texture characteristics.By constantly updating the reliability of image,the lost regions were repaired.Thus,the adaptive restoration of lost regions in damaged image was completed.Simulation results show that,compared with traditional method,the restored image is clearer and more complete through the proposed restoration method.Meanwhile,the repair time is shorter.

关 键 词：破损图像 自适应优化修复 纹理合成 

分 类 号：P391.41[天文地球—地球物理学]