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非局部变分修复法去除高密度椒盐噪声 总被引:1,自引:0,他引:1
分析了中值滤波及其改进型算法在处理高密度椒盐噪声时效果不理想的原因,采用变分修复方法来去除高密度椒盐噪声,基于现有的全变差修复模型提出了非局部全变差修复模型。该模型利用椒盐噪声特点(均匀分布、灰度值为0或255),将噪声点看成是图像中遗失或是破损的点,首先在图像中寻找与噪声点邻域相似的区域,将相似区域的中心像素作为噪声点新的邻域然后对其插值,把图像降噪问题转化为图像修复问题,从而达到去除高密度噪声的目的。实验结果表明:该模型对噪声密度为90%的彩色和灰度图像去噪后,其峰值信噪比为22.85和28.77,在客观评价标准方面优于中值滤波及其改进型算法。该模型能有效去除高密度下的椒盐噪声并较好地恢复图像细节,为图像去除高密度噪声提供了一种新的途径。 相似文献
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针对闪光照相图像受模糊及噪声影响的问题,提出了一种基于约束优化的闪光照相图像重建算法。该算法建立基于平行束投影的正向成像矩阵,并通过嵌入模糊矩阵表达成像过程中的模糊因素,采用最速下降法求解重建问题。在算法中设计了预优矩阵以提高迭代重建速度,利用客体密度值非负、密度分布分段光滑并含有阶跃性边界的先验知识,设计和采用了非负约束、光滑约束及广义变分边界约束条件。对仿真FTO客体图像及实际闪光照相图像的重建结果表明,基于约束优化的重建算法具有良好的边界保持能力及噪声抑制能力,可以有效提高图像重建质量。 相似文献
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图像去噪是遥感图像复原的重要步骤。在去除图像噪声的同时希望尽可能多地保留图像的纹理细节信息。受较差的成像环境和图像数据远距离传输的影响,遥感图像中一般都含有较强的高斯-脉冲混合噪声,而在现有的图像去噪算法中,能够同时去除图像中的高斯-脉冲混合噪声的通用噪声滤波器很少。以非局部平均方法的滤波思想为基础,通过引入邻域相似度评价的概念和脉冲噪声探测器,提出了基于邻域特征匹配的通用噪声滤波器。实验结果表明:基于邻域特征匹配的通用噪声滤波器具备有很好地去除图像高斯-脉冲混合噪声的能力,在去除高斯-脉冲混合噪声的同时能够很好地保持图像的复杂纹理和精细细节,并且便于向DSP/FPGA多处理器平台上移植。 相似文献
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针对雾线先验去雾算法存在的颜色过饱和现象、图像初始透射率估算不准确等问题,提出了一种基于边窗盒子滤波和透射率修正的图像去雾算法。为了解决初始透射率估算不准确带来的边缘细节信息丢失的问题,首先利用非局部总广义变分(TGV)正则化的方法估算初始透射率,并将二阶的非局部总广义变分(TGV)正则器来作为正则项,以确保对由图像颜色和深度之间的噪声和歧义引起的异常值具有鲁棒性。随后利用边窗滤波算法对初始透射率进行优化,从而实现对图像中纹理信息和边缘信息的保留。最后利用大气散射模型和多角度优化后的透射率复原出无雾的原始图像。实现结果表明,本文算法能够解决图像颜色过饱和与边缘处的细节纹理信息丢失的问题,且无色调偏移和光晕效应。在定性评估上,复原后的图像视觉效果好;在定量评估上,本文算法的去雾后图像的评价指标皆高于基于雾线先验算法。 相似文献
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针对飞行时间相机获取深度图像分辨率低,并受成像噪声干扰的问题,提出一种插值深度图和高分辨率彩色图像联合约束的二阶广义全变分(TGV)深度图超分辨率重建方法。首先利用传统插值和多尺度形态学方法进行预处理,获取插值深度图的梯度信息,然后将插值深度图和同场景高分辨率彩色图像两者的梯度信息联合,对二阶TGV模型中的正则化项加以优化:计算各项异性扩散张量时结合插值深度图的梯度信息;引入由插值深度图梯度信息决定的加权因子,控制重建过程中扩散强度。最后通过原始对偶算法完成深度图的超分辨率重建。实验结果表明,本文方法在抑制噪声的基础上,有效保护了深度边缘,可以获得较好的高分辨率深度图像。 相似文献
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结合伪逆关联成像和迭代去噪关联成像,提出了关联成像目标重构的伪逆迭代方法.该方法以伪逆关联成像重构结果为初始值,选取合适的与噪声干扰相关的阈值,通过迭代运算逼近实际的噪声干扰,最终抑制噪声并提高重构图像的峰值信噪比.以峰值信噪比和相关系数为衡量标准,将伪逆迭代关联成像的重构结果与差分关联成像、伪逆关联成像进行对比分析.仿真实验结果表明,伪逆迭代方法的峰值信噪比较伪逆关联成像方法、差分关联成像方法分别高出约1.0dB、3.1dB,同时其相关系数、视觉效果也有所改善,验证了该方法的有效性. 相似文献
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鬼成像方案实现所需设备、成像的质量以及成像所花的时间是决定鬼成像技术可实用化的重要因素. 本文提出一种针对多散斑图的差分压缩鬼成像方案. 该方案通过连续探测多个独立的散斑图, 降低了热光鬼成像方案对探测器高时间分辨力的要求; 通过采用差分方法, 抑制了背景噪声和其他噪声源的干扰; 通过使用压缩感知重建算法, 有效地降低了鬼成像所需时间并同时提升成像的质量. 数值仿真结果表明, 对于二灰度“N” 图, 本方案在8000次的采样情形下与多散斑图鬼成像方案35000次采样的结果相比, 均方误差降低了96.9%、峰值信噪比提升15.1 dB. 对于八灰度“Pepper”图, 本方案与多散斑图鬼成像方案相比, PSNR提升11.4 dB. 本方案可降低探测设备的要求、提高成像质量、降低重建时间, 具有广阔的应用前景. 相似文献
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分辨率是成像系统的一个重要参数, 获得高分辨率图像一直是鬼成像系统的一个目标. 本文提出了以成像系统点扩散函数作为先验知识, 基于稀疏测量的超分辨压缩感知鬼成像重建模型. 搭建了一套计算鬼成像实验装置, 用于验证该模型对于提高鬼成像系统分辨率的有效性, 并与传统的鬼成像计算模型进行了对比. 实验表明, 利用该模型可突破成像系统衍射极限分辨率的限制, 得到超分辨鬼成像.
关键词:
鬼成像
压缩感知
超分辨
稀疏测量 相似文献
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In this paper, we propose an image encryption technique to simultaneously encrypt double or multiple images into one encrypted image using computational integral imaging (CII) and fractional Fourier transform (FrFT). In the encryption, each of the input plane images are located at different positions along a pickup plane, and simultaneously recorded in the form of an elemental image array (EIA) through a lenslet array. The recorded EIA to be encrypted is multiplied by FrFT with two different fractional orders. In order to mitigate the drawbacks of occlusion noise in computational integral imaging reconstruction (CIIR), the plane images can be reconstructed using a modified CIIR technique. To further improve the solution of the reconstructed plane images, a block matching algorithm is also introduced. Numerical simulation results verify the feasibility and effectiveness of the proposed method. 相似文献
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Yi LiuZhi-guo Gui 《Optik》2012,123(23):2174-2178
Low-dose CT imaging has been particularly used in modern medical practice for its advantage on reducing the radiation dose to patients. However, excessive quantum noise is present in low dose X-ray imaging along with the decrease of the radiation dose; thus, there are obvious streak-like artifacts in reconstructed images. The statistical iterative reconstruction approach applied to the noisy sinogram before a filtered back-projection (FBP) is a resolution to deal with the noisy problem. In this paper, the statistical property of the noise sinogram was considered to achieve a satisfactory image reconstruction and a statistical iterative method with energy minimization was proposed to address the problem of streak-like artifacts. Simulations were performed and indicated that the proposed method could suppress noise and obviously decrease streak-like artifacts in reconstructed images. 相似文献
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We propose a computed tomography (CT) for the incoherent optical system based on a generalized analytic reconstruction method from discrete samples (GARDS), by which an object image defined in a continuous space is reconstructed from discrete images acquired through the imaging system. We apply this method to reconstruct depth structures of micro-specimens using conventional fluorescence microscopes. Also, we discuss the optimal sampling distance in the depth direction through a generalized singular value decomposition of a continuous-to-discrete imaging system. We next apply the GARDS-based CT method to a double-axis microscope, in which two microscopes are set up perpendicularly to cover a missing frequency band of each microscopic imaging system. We show simulation results which verify the effectiveness of our proposed method. 相似文献
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In photoacoustic imaging (PAI), reconstruction from sparse-view sampling data is a remaining challenge in the cases of fast or real-time imaging. In this paper, we present our study on a total variation based gradient descent (TV-GD) algorithm for sparse-view PAI reconstruction. This algorithm involves the total variation (TV) method in compressed sensing (CS) theory. The objective function of the algorithm is modified by adding the TV value of the reconstructed image. With this modification, the reconstructed image could be closer to the real optical energy distribution map. Additionally in the proposed algorithm, the photoacoustic data is processed and the image is updated individually at each detection point. In this way, the calculation with large matrix can be avoided and a more frequent image update can be obtained. Through the numerical simulations, the proposed algorithm is verified and compared with other reconstruction algorithms which have been widely used in PAI. The peak signal-to-noise ratio (PSNR) of the image reconstructed by this algorithm is higher than those by the other algorithms. Additionally, the convergence of the algorithm, the robustness to noise and the tunable parameter are further discussed. The TV-based algorithm is also implemented in the in vitro experiment. The better performance of the proposed method is revealed in the experiments results. From the results, it is seen that the TV-GD algorithm may be a practical and efficient algorithm for sparse-view PAI reconstruction. 相似文献
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Jin-Fen Liu 《中国物理 B》2022,31(8):84202-084202
Ghost imaging (GI) offers great potential with respect to conventional imaging techniques. However, there are still some obstacles for reconstructing images with high quality, especially in the case that the orthogonal measurement matrix is impossible to construct. In this paper, we propose a new scheme based on the orthogonal-triangular (QR) decomposition, named QR decomposition ghost imaging (QRGI) to reconstruct a better image with good quality. In the scheme, we can change the randomly non-orthogonal measurement matrix into orthonormal matrix by performing QR decomposition in two cases. (1) When the random measurement matrix is square, it can be firstly decomposed into an orthogonal matrix $\bm Q$ and an upper triangular matrix $\bm R$. Then let the off-diagonal values of $\bm R$ equal to 0.0, the diagonal elements of $\bm R$ equal to a constant $k$, where $k$ is the average of all values of the main diagonal, so the resulting measurement matrix can be obtained. (2) When the random measurement matrix is with full rank, we firstly compute its transpose, and followed with above QR operation. Finally, the image of the object can be reconstructed by correlating the new measurement matrix and corresponding bucket values. Both experimental and simulation results verify the feasibility of the proposed QRGI scheme. Moreover, the results also show that the proposed QRGI scheme could improve the imaging quality comparing to traditional GI (TGI) and differential GI (DGI). Besides, in comparison with the singular value decomposition ghost imaging (SVDGI), the imaging quality and the reconstruction time by using QRGI are similar to those by using SVDGI, while the computing time (the time consuming on the light patterns computation) is substantially shortened. 相似文献
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This paper describes a novel image filtering method that removes random-valued impulse noise superimposed on a natural color image. In impulse noise removal, it is essential to employ a switching-type filtering method, as used in the well-known switching median filter, to preserve the detail of an original image with good quality. In color image filtering, it is generally preferable to deal with the red (R), green (G), and blue (B) components of each pixel of a color image as elements of a vectorized signal, as in the well-known vector median filter, rather than as component-wise signals to prevent a color shift after filtering. By taking these fundamentals into consideration, we propose a switching-type vector median filter with non-local processing that mainly consists of a noise detector and a noise removal filter. Concretely, we propose a noise detector that proactively detects noise-corrupted pixels by focusing attention on the isolation tendencies of pixels of interest not in an input image but in difference images between RGB components. Furthermore, as the noise removal filter, we propose an extended version of the non-local median filter, we proposed previously for grayscale image processing, named the non-local vector median filter, which is designed for color image processing. The proposed method realizes a superior balance between the preservation of detail and impulse noise removal by proactive noise detection and non-local switching vector median filtering, respectively. The effectiveness and validity of the proposed method are verified in a series of experiments using natural color images. 相似文献
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本文提出一种采用非局部主成分分析的极大似然估计去噪方法.首先采用非局部主成分分析算法来计算像素邻域间的灰度值和纹理结构相似性,然后通过极大似然估计方法估计最优复原图像.本方法使用非局部主成分分析克服现有局部性去噪方法模糊边界等缺陷,引入极大似然估计方法来改进现有非局部均值的简单加权均值去噪处理,从而提高对图像细节信息的复原能力.最后分别使用本文方法、非局部均值和局部极大似然估计三种去噪方法,在不同噪音大小和不同几何纹理复杂度的图像中进行定性和定量的去噪实验.结果表明,本文方法可在保持图像细节和纹理信息的情况下有效去噪,较之现有方法效果更好. 相似文献