基于稀疏表示的局部模糊图像目标边缘盲检测  

Blind Detection of Object Edges in Locally Blurred Images Based on Sparse Representation

作　　者：冯洋洋 青华 FENG Yang-yang;QING Hua(College of Software Engineering,Zhengzhou University of Industry Technology,Xinzheng Henan 451100,China;College of Software Engineering,Zhengzhou University of Light Industry,Zhengzhou Henan 450002,China)

机构地区：[1]郑州工业应用技术学院软件学院,河南新郑451100 [2]郑州轻工业大学软件学院,河南郑州450002

出　　处：《计算机仿真》2024年第11期475-480,共6页Computer Simulation

摘　　要：传统模糊图像检测恢复算法面对复杂图像时,存在模糊边缘检测准确率低,泛化力较差的问题,即无法保证模糊目标恢复后的信息传递效果。为提高模糊图块的局部边缘检测性能,基于图块边缘稀疏表示与结构相似性特点,提出一种EBD模糊图像目标边缘盲检测算法。算法首先采用灰度与归一法对图像数据进行预处理,以提升图像计算效率;然后通过Nelder-M寻优法估计图像H-Laplace分布的最优参数,完成模糊图块边缘检测与特征提取;接着利用OMP算法求解稀疏系数,对模糊图块进行图像重构;最后利用降采样的方法,将模糊图块进行缩放与多尺度组合,并将两次重构后的图像进行融合,完成局部模糊目标恢复。模糊图像盲检测恢复仿真主观结果表明,与其它基线算法相比,EBD算法检测恢复后,图像亮度更高、纹理度更清晰;仿真结果客观分析显示,较其它基线算法而言,EBD算法的P参数整体提升了34.60%,E参数降低32.92%,S参数增加3.40%,即图像恢复真实性更高,模糊目标检测更准确。综上,EBD模糊图像目标边缘盲检测算法通过图像稀疏表示提高了模糊图块的检测力,且有效提供了图像恢复力,在计算机视觉仿真领域中,具有重要的研究价值。In the face of complex images,the traditional fuzzy image detection and restoration algorithm has the problems of low accuracy of fuzzy edge detection and poor generalization ability,that is,it can not guarantee the information transmission effect after the restoration of fuzzy objects.In order to improve the performance of local edge de-tection for fuzzy patches,a blind edge detection algorithm based on sparse representation and structural similarity is proposed.Firstly,the image data is preprocessed by using the gray and normalization method to improve the computational efficiency of the image,and then the optimal parameters of the H-La place distribution of the image are esti-mated by the Nelder-M optimization method to complete the edge detection and feature extraction of the fuzzy image block,and then the sparse coefficient is solved by the OMP algorithm to reconstruct the fuzzy image block.Finally,by using the method of down-sampling,the blurred image blocks are zoomed and combined in multi-scale,and the ima-ges reconstructed twice are fused to complete the restoration of local blurred objects.Subjective simulation results of blind detection and restoration of blurred images show that compared with other baseline algorithms,the EBD algorithm has higher brightness and clearer texture after detection and restoration.The objective analysis of the simulation results shows that compared with other baseline algorithms,the P index of EBD algorithm is improved by 34.60%,the E index is reduced by 32.92%,and the S index is increased by 3.40%,that is to say,the image restoration is more realistic and the fuzzy target detection is more accurate.To sum up,the blind edge detection algorithm of EBD blurred image target improves the detection power of blurred image blocks through image sparse representation,and effectively provides image resilience,which has important research value in the field of computer vision simulation.

关 键 词：稀疏表示 边缘目标盲检测 图像去模糊 

分 类 号：TP391.9[自动化与计算机技术—计算机应用技术]