共查询到18条相似文献,搜索用时 109 毫秒
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多正则化形式的超分辨率图像重建 总被引:1,自引:0,他引:1
为了抑制超分辨图像重建过程中的振铃锯齿效应,本文提出一种多正则化形式的超分辨率重建算法。文章首先给出了图像降质模型并推导出了图像重构约束项。利用重构项直接对低分辨率图像进行重建,获得的高分辨图像会有锯齿和振铃效应。针对此问题,本文利用自回归模型和滤波器组先验来正则化重建过程。自回归模型用来恢复图像局部细节描述,与此同时本文利用自然图像块的聚类集来估计自适应自回归模型参数。滤波器组先验用来约束重建图像的边缘,使得获取的高分辨率的图像边缘更加锐利。最后通过实验定性与定量的分析,证实了本文算法优于其他具有竞争力的算法。 相似文献
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针对电容层析成像系统图像重建过程中Tiknonov正则化解过度光滑引起的重建图像细节信息丢失问题,引入l_(2,p)(0p≤1)的混合范数作为正则化算法的数据项和正则化项。混合范数l_(2,p)利用了欧氏范数l_2的光滑性和分数范数l_p(0p≤1)的稀疏性,不仅比范数L_(2,1)具有更好的联合稀疏性,对噪声的抗干扰性也更强,进而针对l_(2,p)矩阵范数的非凸、非Lipschitz连续问题提出一种新的电容层析成像图像重建模型。实验结果表明,基于矩阵混合范数l_(2,p)极小化优化模型的正则化算法相比牛顿迭代、奇异值分解、共轭梯度算法具有更强的适应性,更高的图像分辨率及更好的成像质量。 相似文献
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《中国计量学院学报》2017,(4):478-484
提出了一种参数自适应的图像超分辨率重建方法.在基于稀疏表示的图像超分辨率重建的经典算法模型框架下,正则化参数可以根据每个图像补丁本身情况自适应地确定,从而克服了人为选择参数且所有补丁参数需一致的缺点,因此使图像重建效果得到提升.实验结果表明,我们所提方法在不同尺寸扩大因子和噪声环境下都优于人工确定参数的情形,三种评价指标均表明所提方法是有效的. 相似文献
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碳纤维复合材料(carbon fiber reinforced polymer,CFRP)由于其轻质高强、抗疲劳等优势被广泛应用于航空航天领域。为确保材料使用的安全性,碳纤维复合材料的有效检测尤为重要。近年来,电阻抗层析成像(electrical impedance tomography,EIT)因其低成本、无辐射等优点已成为一种新兴的损伤监测方法并受到了广泛关注。针对电阻抗层析成像逆问题求解具有严重的病态性,提出了一种基于改进低秩稀疏正则化的电阻抗层析成像算法。首先,引入L_(p)伪范数,通过调节p的值来增强解的稀疏性、提高图像重建精度;其次,采用核范数作为解的低秩约束能有效利用先验信息提高重建质量;最后,通过分裂布雷格曼方法求解,增强算法的实时性,使成像速度保持在0.06 s。仿真与试验结果表明,改进低秩稀疏正则化算法能有效改善电极伪影、呈现出更加清晰的损伤细节并且具有较强的鲁棒性、实效性和适用性。 相似文献
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针对传统TV去噪复原算法以梯度模值作为图像的边缘检测算子,无法清晰地识别边缘和灰度渐变区及去除平坦区内的孤立噪声的问题,提出了一种基于局部坐标二次微分的边缘检测算子对传统模型进行改进。改进后的模型能根据各像素点的新检测算子信息,自适应选取复原模型中决定扩散强弱的参数,并且利用图像局部信息对正则化项和保真项进行加权。同时在数值实现上,采用一种基于梯度矢量的方向变化的方法来实现散度离散化,以更加有效地保留图像的局部细节信息。数值试验表明,该算法在克服灰度渐变区内的阶梯效应和保留图像的细节边缘方面明显优于传统算法。 相似文献
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In view of the common existing problems in present video-to-video super-resolution reconstruction, this paper proposes a pioneering video-to-video super-resolution reconstruction algorithm based on segmentation and space–time regularisation to solve these problems. First, a video-to-video super-resolution reconstruction algorithm based on segmentation is proposed to eliminate reconstructed temporal ringing and to improve the times of reconstruction. Second, considering that image mosaic is involved in our proposed reconstruction algorithm, an improved fade-in and fade-out method is proposed to make the mosaic image looks more natural. At last, an improved space–time regularisation algorithm is put forward to remove noise and preserve image edge at the same time. Using several experiments, we prove that the proposed algorithm can achieve state-of-the -art reconstruction effect. 相似文献
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S. E. EL-Khamy M. M. Hadhoud M. I. Dessouky B. M. Salam 《Journal of Modern Optics》2013,60(8):1027-1039
An algorithm for blind super-resolution reconstruction of a single image from multiple degraded observations is developed. The algorithm depends on estimating the 2D greatest common divisor (GCD) between each observation and a combinational image generated by a weighted averaging process of the available observations. The purpose of generating this combinational image is to obtain a new image with a higher signal to noise ratio, and a blurring operator that is co-prime with all the blurring operators of the available observations. The 2D GCD is then estimated between the new image and each observation and thus the effect of noise on the estimation process is reduced. The results of each 2D GCD process are fused to form a single reconstructed image, which is then interpolated subject to local regularization to form a high-resolution (HR) image. Results show that the proposed algorithm succeeds in estimating an HR image from noisy blurred observations in the case of relatively co-prime unknown blurring operators. 相似文献
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Underwater imaging is widely used in ocean, river and lake exploration, but it
is affected by properties of water and the optics. In order to solve the lower-resolution
underwater image formed by the influence of water and light, the image super-resolution
reconstruction technique is applied to the underwater image processing. This paper
addresses the problem of generating super-resolution underwater images by
convolutional neural network framework technology. We research the degradation model
of underwater images, and analyze the lower-resolution factors of underwater images in
different situations, and compare different traditional super-resolution image
reconstruction algorithms. We further show that the algorithm of super-resolution using
deep convolution networks (SRCNN) which applied to super-resolution underwater
images achieves good results. 相似文献
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目的为了解决当前稀疏表示的超分辨率算法效果依赖参与训练的数据的问题,结合图像的自相似性,提出一种基于自相似性与稀疏表示相结合的超分辨率算法。方法算法利用图像的多维自相似性,构建多维图像金字塔,采用改进的相似块搜索策略,得到对应的高低分辨率图像块作为训练样本,然后对样本进行字典训练,最后根据稀疏表示得到超分辨率图像。结果实验结果显示,文中算法在峰值信噪比(PSNR)和结构相似度(SSIM)上优于其他算法,对于实验图像而言,PSNR平均提升了0.5 dB。结论提出的超分辨率算法未引入外部数据库,具有较好的效果,能够用于超分辨率重建。 相似文献
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Diffuse tomography with near-infrared light has biomedical application for imaging hemoglobin, water, lipids, cytochromes, or exogenous contrast agents and is being investigated for breast cancer diagnosis. A Newton-Raphson inversion algorithm is used for image reconstruction of tissue optical absorption and transport scattering coefficients from frequency-domain measurements of modulated phase shift and light intensity. A variant of Tikhonov regularization is examined in which radial variation is allowed in the value of the regularization parameter. This method minimizes high-frequency noise in the reconstructed image near the source-detector locations and can produce constant image resolution and contrast across the image field. 相似文献
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提出一种电学层析成像(ECT)图像重建优化算法。通过将传统正则化算法转化为最小二乘问题进行求解,结合lp范数逼近正则化最小化问题,利用重新加权的方法进行迭代计算。以油-气两相流模型进行仿真及静态实验,将所提出的优化算法与常用的LBP、Landweber迭代及Tikhonov正则化算法进行对比。结果表明,与常用算法相比,采用该优化算法对管道中心物体及多物体分布流型进行图像重建,其图像相对误差均为最低,且重建图像的形状保真度明显提高。 相似文献