基于图像组的稀疏正则化图像复原方法采用自适应的结构组字典来代替传统的基于整幅图像块的学习字典, 既能够更好的学习局部特征又显著降低字典学习的时间复杂度; 然而, 因算法中的一些参数还未优化, 使得算法复杂度还比较高。因此, 本文提出了基于粗糙度的自适应图像组的稀疏正则化图像复原方法。首先, 计算图像的全局粗糙度和局部粗糙度; 然后, 根据全局的粗糙度计算自适应调整正则化的迭代次数, 根据局部的粗糙度调整学习字典所需的样本数; 最后, 将自适应调整出的参数应用于基于图像组的稀疏正则化的图像复原中。将本文所提出的方法应用到不同平滑度图像的去文字图像复原案例中, 实验结果表明, 在保证相近的复原效果下, 能够大幅度提升效率, 尤其在较为平滑的图像中能够达到接近30倍的加速比。

The sparse regularized image restoration method based on animage group adopts the adaptive structure group dictionary to replace the traditional learning dictionary based on the entireimage block.However, because some parameters in the algorithm have not been optimized, the complexity of the algorithm remains relatively high.Therefore, this study proposed a sparse regularization image restoration method based on an adaptive image group in terms of roughness.First, global and local image roughnesses were calculated.Then, the number of self-adaptive regularization iterations was calculated according to the global roughness, and the number of samples required for learning the dictionary was adjusted based on the local roughness.Finally, the adaptive parameters were applied to the process of sparse regularization image restoration based on an image group.The method proposed in this study was applied to a case involving image restoration of text removal for images with different degrees of smoothness. The experimental results show that the efficiency of image restoration can be greatly improved when a similar restoration effect is guaranteed, particularly in relatively smooth images, where the speed-up ratio can reach nearly 30 times.