摘要
针对图像修复结果中存在的结构连续性和纹理清晰性较差的问题,提出了一种基于自适应相似组的图像修复算法。区别于传统的以单一图像块或固定数目图像块作为修复单元的方法,该算法根据自然图像中纹理区和结构区的不同特点,自适应地选取不同数目的相似图像块,构造自适应相似组;然后以相似组作为基本单元,学习自适应字典,并构造基于稀疏表示的图像修复模型;最后,采用Split Bregman Iteration算法高效地求解目标代价函数。实验结果表明,与基于图像块的图像修复算法和图像块组稀疏表示(GSR)算法相比,该算法在峰值信噪比(PSNR)上平均提高了0.94~4.34 dB,在结构相似性指数(SSIM)上平均提高了0.006 9~0.034 5,同时,修复速度分别是对比算法的2.51倍和3.32倍。
Focusing on the problem of object structure discontinuity and poor texture detail occurred in image inpainting,an inpainting algorithm based on self-adaptive group was proposed. Different from the traditional method which uses a single image block or a fixed number of image blocks as the repair unit, the proposed algorithm adaptively selects different number of similar image blocks according to the different characteristics of the texture area to construct self-adaptive group. A selfadaptive dictionary as well as a sparse representation model was established in the domain of self-adaptive group. Finally, the target cost function was solved by Split Bregman Iteration. The experimental results show that compared with the patch-based inpainting algorithm and Group-based Sparse Representation（ GSR） algorithm, the Peak Signal-to-Noise Ratio（PSNR） and the Structural SIMilarity（SSIM） index are improved by 0. 94-4. 34 dB and 0.006 9-0.034 5 respectively; meanwhile, the proposed approach can obtain image inpainting speed-up of 2.51 and 3.32 respectively.
出处
《计算机应用》
CSCD
北大核心
2017年第4期1169-1173,共5页
journal of Computer Applications
基金
国家自然科学基金资助项目(61501334)~~
关键词
图像修复
稀疏表示
自适应相似组
学习字典
自适应性
image inpainting
sparse representation
self-adaptive group
dictionary learning
adaptivity
