针对一般超分辨率算法无法在重建效果和运行速度上取得较好的平衡,结合结构相似性提出了一种具有保边性的迭代反投影算法(iterative back projection,IBP)的超分辨率算法。兼顾算法运行速度,在传统的IBP基础上添加各项异性扩散,使之具...针对一般超分辨率算法无法在重建效果和运行速度上取得较好的平衡,结合结构相似性提出了一种具有保边性的迭代反投影算法(iterative back projection,IBP)的超分辨率算法。兼顾算法运行速度,在传统的IBP基础上添加各项异性扩散,使之具有边缘增强和保边效果。为了结合更多的先验知识,将保边IBP和结构相似性结合在一起,使重建效果更加自然细腻。采用峰值信噪比和结构相似性度量评价算法效果。从实验结果数据可以看出,算法具有最高的PSNR值和SSIM值,其中SSIM的值最高可以达到0.95,非常接近于1。从实验结果图可以看出,算法的纹理保留的最好,图像更自然和准确。而且运行速度没有严重落后于传统IBP,具有很好的实用性。展开更多
In this paper, we introduce some new classes of the totally quasi-G-asymptotically nonexpansive mappings and the totally quasi-G-asymptotically nonexpansive semigroups. Then, with the generalized f-projection operator...In this paper, we introduce some new classes of the totally quasi-G-asymptotically nonexpansive mappings and the totally quasi-G-asymptotically nonexpansive semigroups. Then, with the generalized f-projection operator, we prove some strong convergence theorems of a new modified Halpern type hybrid iterative algorithm for the totally quasi-G-asymptotically nonexpansive semigroups in Banach space. The results presented in this paper extend and improve some corresponding ones by many others.展开更多
Traditional direction of arrival(DOA)estimation methods based on sparse reconstruction commonly use convex or smooth functions to approximate non-convex and non-smooth sparse representation problems.This approach ofte...Traditional direction of arrival(DOA)estimation methods based on sparse reconstruction commonly use convex or smooth functions to approximate non-convex and non-smooth sparse representation problems.This approach often introduces errors into the sparse representation model,necessitating the development of improved DOA estimation algorithms.Moreover,conventional DOA estimation methods typically assume that the signal coincides with a predetermined grid.However,in reality,this assumption often does not hold true.The likelihood of a signal not aligning precisely with the predefined grid is high,resulting in potential grid mismatch issues for the algorithm.To address the challenges associated with grid mismatch and errors in sparse representation models,this article proposes a novel high-performance off-grid DOA estimation approach based on iterative proximal projection(IPP).In the proposed method,we employ an alternating optimization strategy to jointly estimate sparse signals and grid offset parameters.A proximal function optimization model is utilized to address non-convex and non-smooth sparse representation problems in DOA estimation.Subsequently,we leverage the smoothly clipped absolute deviation penalty(SCAD)function to compute the proximal operator for solving the model.Simulation and sea trial experiments have validated the superiority of the proposed method in terms of higher resolution and more accurate DOA estimation performance when compared to both traditional sparse reconstruction methods and advanced off-grid techniques.展开更多
文摘针对一般超分辨率算法无法在重建效果和运行速度上取得较好的平衡,结合结构相似性提出了一种具有保边性的迭代反投影算法(iterative back projection,IBP)的超分辨率算法。兼顾算法运行速度,在传统的IBP基础上添加各项异性扩散,使之具有边缘增强和保边效果。为了结合更多的先验知识,将保边IBP和结构相似性结合在一起,使重建效果更加自然细腻。采用峰值信噪比和结构相似性度量评价算法效果。从实验结果数据可以看出,算法具有最高的PSNR值和SSIM值,其中SSIM的值最高可以达到0.95,非常接近于1。从实验结果图可以看出,算法的纹理保留的最好,图像更自然和准确。而且运行速度没有严重落后于传统IBP,具有很好的实用性。
文摘In this paper, we introduce some new classes of the totally quasi-G-asymptotically nonexpansive mappings and the totally quasi-G-asymptotically nonexpansive semigroups. Then, with the generalized f-projection operator, we prove some strong convergence theorems of a new modified Halpern type hybrid iterative algorithm for the totally quasi-G-asymptotically nonexpansive semigroups in Banach space. The results presented in this paper extend and improve some corresponding ones by many others.
基金supported by the National Science Foundation for Distinguished Young Scholars(Grant No.62125104)the National Natural Science Foundation of China(Grant No.52071111).
文摘Traditional direction of arrival(DOA)estimation methods based on sparse reconstruction commonly use convex or smooth functions to approximate non-convex and non-smooth sparse representation problems.This approach often introduces errors into the sparse representation model,necessitating the development of improved DOA estimation algorithms.Moreover,conventional DOA estimation methods typically assume that the signal coincides with a predetermined grid.However,in reality,this assumption often does not hold true.The likelihood of a signal not aligning precisely with the predefined grid is high,resulting in potential grid mismatch issues for the algorithm.To address the challenges associated with grid mismatch and errors in sparse representation models,this article proposes a novel high-performance off-grid DOA estimation approach based on iterative proximal projection(IPP).In the proposed method,we employ an alternating optimization strategy to jointly estimate sparse signals and grid offset parameters.A proximal function optimization model is utilized to address non-convex and non-smooth sparse representation problems in DOA estimation.Subsequently,we leverage the smoothly clipped absolute deviation penalty(SCAD)function to compute the proximal operator for solving the model.Simulation and sea trial experiments have validated the superiority of the proposed method in terms of higher resolution and more accurate DOA estimation performance when compared to both traditional sparse reconstruction methods and advanced off-grid techniques.
