利用HSSIM和残差比阈值的3维激光扫描图像去噪

为了更好地实现3维激光扫描图像的去噪,提出一种融合直方图结构相似度(HSSIM)和残差比阈值的改进稀疏去噪算法。利用初始化过完备字典进行稀疏分解,以相似因子代替重构误差作为保真项,利用残差比阈值作为正交匹配追踪算法的迭代终止条件对图像进行去噪,获得了去噪后图像的峰值信噪比及HSSIM指标。结果表明,与基于db2小波变换、多尺度曲波变换和离散余弦变换的去噪方法相比,该算法能获得更好的峰值信噪比和HSSIM数据。在有效去除图像噪声的同时还能更有效地保留图像的细节特征。     

多小波变换在夜视图像融合算法中的应用

从微光图像和红外图像的特点出发,提出了一种基于多小波变换的夜视图像融合算法.该方法先对夜视图像进行多小波变换得到各子带的多小波系数,其低频系数采用自适应加权融合算子进行融合,高频系数先进行阈值去噪和子带增强,再采用基于频带方向的融合算子进行融合,经多小波逆变换后得到融合图像.实验结果表明,本文算法与单小波融合算法、传统融合算法相比,得到的图像能更好地突出图像的边缘特征,增强图像的可视性和清晰度,并在信息熵、峰值信噪比等客观性能指标上取得了显著的改善.     

多视点纹理加深度编码的联合码率控制方法

码率控制技术是多视点视频编码和传输中一个关键的问题。为了提高三维(3D)视频的整体显示质量,包括虚拟视点质量和编码视点质量,提出一种多视点纹理加深度编码的联合码率控制方法。该算法研究了纹理和深度的关系,采用基于模型方法确定最优的纹理和深度之间的码率比例。根据各个视点编码结果的统计规律,不同的视频序列采用不同的视点间比特分配比例。实验结果表明,与目前流行的多视点码率控制算法相比,该算法在计算复杂度基本保持不变的情况下,平均码率控制误差在0.6%以内,客观质量PSNR最高可提高0.65 dB。     

基于小波域Curvelet变换的湍流图像去噪算法

为了提高湍流图像的空间分辨率,提出了一种基于小波域Curvelet变换(wavelet domain Curvelet transform,WDCT)的湍流图像去噪算法。该算法根据湍流退化图像噪声的统计特性,结合贝叶斯萎缩方法优化阈值选择。首先,对含噪湍流图像进行单层二维离散小波变换,接着提取高频系数并对它作快速离散Curvelet变换,最后根据贝叶斯准则估计阈值T,改进阈值的自适应选取方法,获得最优阈值,最后给出湍流图像去噪实现过程。为验证本文算法,根据客观评价标准峰值信噪比(peak signal to noise ratio,PSNR)和均方根误差(mean square error,MSE),对模拟图像和实测湍流图像进行去噪实验。与DWT-NABayesShrink算法、UWT算法相比,视觉效果更好,PSNR值分别提高7.27%和4.92%,MSE值分别降低26.3%和23.1%。本文算法得到较清晰的目标图像,对湍流退化图像去噪有一定的应用价值。     

Demosaicing enhancement using pixel-level fusion

Bayer pattern has been widely used in commercial digital cameras. In NASA’s mast camera (Mastcams) onboard the Mars rover Curiosity, Bayer pattern has also been used in capturing the RGB bands. It is well known that debayering, also known as demosaicing in the literature, introduces artifacts such as false colors and zipper edges. In this paper, we first present four fusion approaches, including weighted and the well-known alpha-trimmed mean filtering approaches. Each fusion approach combines demosaicing results from seven debayering algorithms in the literature, which are selected based on their performance mentioned in other survey papers and the availability of open source codes. Second, we present debayering results using two benchmark image data sets: IMAX and Kodak. It was observed that none of the seven algorithms in the literature can yield the best performance in terms of peak signal-to-noise ratio (PSNR), CIELAB score, and subjective evaluation. Although the fusion algorithms are simple, it turns out that the debayering performance can be improved quite dramatically after fusion based on our extensive evaluations. In particular, the average PSNR improvements of the weighted fusion algorithm over the best individual method are 1.1 dB for the IMAX database and 1.8 dB for the Kodak database, respectively. Third, we applied the various algorithms to 36 actual Mastcam images. Subjective evaluation indicates that the fusion algorithms still work well, but not as good as the existing debayering algorithm used by NASA.     

A unified framework for multi-sensor HDR video reconstruction

One of the most successful approaches to modern high quality HDR-video capture is to use camera setups with multiple sensors imaging the scene through a common optical system. However, such systems pose several challenges for HDR reconstruction algorithms. Previous reconstruction techniques have considered debayering, denoising, resampling (alignment) and exposure fusion as separate problems. In contrast, in this paper we present a unifying approach, performing HDR assembly directly from raw sensor data. Our framework includes a camera noise model adapted to HDR video and an algorithm for spatially adaptive HDR reconstruction based on fitting of local polynomial approximations to observed sensor data. The method is easy to implement and allows reconstruction to an arbitrary resolution and output mapping. We present an implementation in CUDA and show real-time performance for an experimental 4 Mpixel multi-sensor HDR video system. We further show that our algorithm has clear advantages over existing methods, both in terms of flexibility and reconstruction quality.     

Efficient wavelet-based image denoising algorithm

Characterising the statistics of wavelet coefficients is a critical issue in image compression and denoising. Many powerful approaches have been investigated, but accurate modelling suffers from high computational complexity. In this work an efficient adaptive algorithm to capture the dependency of both inner and inter scale wavelet coefficients is proposed. Experimental results show that compared with other algorithms, in the case of higher noise variance, greater PSNR performance gain may be obtained     

Video object segmentation by 2-D mesh-based motion analysis

Video object extraction is a key technology in content-based video coding.A novel video object extracting algorithm by two Dimensional (2-D) mesh-based motion analysis is proposed in this paper.Firstly,a 2-D mesh fitting the original frame image is obtained via feature detection algorithm. Then,higher order statistics motion analysis is applied on the 2-D mesh representation to get an initial motion detection mask.After post-processing,the final segmenting mask is quickly obtained.And hence the video object is effectively extracted.Experimental results show that the proposed algorithm combines the merits of mesh-based segmenting algorithms and pixel-based segmenting algorithms,and hereby achieves satisfactory subjective and objective performance while dramatically increasing the segmenting speed.     

基于双域滤波的三维块匹配视频去噪算法

将双域滤波和三维块匹配算法相结合,充分利用了视频序列的时空频域的相关性,进行视频去噪。将三维块匹配视频去噪算法中小波阈值去噪得到的图像替换原引导图像,抑制了双域滤波算法基本层不平滑的问题。参考残差矩阵的能量谱对短时傅里叶变换系数进行阈值收缩,减少细节层的噪声残留。实验结果表明,本算法有效地解决了双域滤波算法的基本层不平滑、细节层有噪声残留的问题,主观和客观对比也表明,本算法有着较为优秀的去噪效果。     

小波阈值图像去噪算法及MATLAB仿真实验

本文研究了小波闽值图像的去噪方法,并与其它图像去噪方法进行了比较。对lena图像进行MATLAB仿真实验,得到了主观效果图和客观效果的PSNR。研究发现,小波阈值图像去噪无论主观效果还是客观效果都优于其他图像去噪方法。     

BM3D视频去噪算法实现与评估

一些传统的图像视频去噪算法,会在滤除噪声的同时引入人工噪声,存在很大的局限性。而基于块匹配和三维变换域滤波(BM3D)的图像视频去噪算法则采用了不同的去噪策略。对BM3D算法进行了深入的研究并实现了算法,对算法进行了深层次的评估。通过评估结果,证明了BM3D算法在视频去噪方面取得了非常好的效果。     

一种空间自适应小波门限去噪算法

提出了一种空间自适应小波门限去噪算法,该算法在小波域对含噪小波系数做两次自适应去噪,两次自适应门限分别基于最大似然(ML)方差估计和最大后验概率(MAP)方差估计.仿真结果表明,该算法与其它自适应门限去噪算法相比,去噪后的图象具有更高的峰值信噪比(PSNR).     

Color monogenic wavelet transform for multichannel image denoising

This paper proposes an effective color image denoising algorithm using the combination color monogenic wavelet transform (CMWT) with a trivariate shrinkage filter. The CMWT coefficients are one order of magnitude with three phases: two phases encode the local color information while the third contains geometric information relating to texture within the color image. In the CMWT domain, a trivariate Gaussian distribution is applied to capture statistical dependencies between the CMWT coefficients, and then a trivariate shrinkage filter is derived using a maximum a posteriori estimator. The performance of the proposed algorithm is experimentally verified using a variety of color test images with a range of noise levels in terms of PSNR and visual quality. The experimental results demonstrate that the proposed algorithm is equal to or better than current state-of-the-art algorithms in both visual and quantitative performance.     

Estimation of 2-D noisy fractional Brownian motion and itsapplications using wavelets

The two-dimensional (2-D) fractional Brownian motion (fBm) model is useful in describing natural scenes and textures. Most fractal estimation algorithms for 2-D isotropic fBm images are simple extensions of the one-dimensional (1-D) fBm estimation method. This method does not perform well when the image size is small (say, 32x32). We propose a new algorithm that estimates the fractal parameter from the decay of the variance of the wavelet coefficients across scales. Our method places no restriction on the wavelets. Also, it provides a robust parameter estimation for small noisy fractal images. For image denoising, a Wiener filter is constructed by our algorithm using the estimated parameters and is then applied to the noisy wavelet coefficients at each scale. We show that the averaged power spectrum of the denoised image is isotropic and is a nearly 1/f process. The performance of our algorithm is shown by numerical simulation for both the fractal parameter and the image estimation. Applications to coastline detection and texture segmentation in a noisy environment are also demonstrated.     

Color image denoising with wavelet thresholding based on human visual system model

Recent research in transform-based image denoising has focused on the wavelet transform due to its superior performance over other transform. Performance is often measured solely in terms of PSNR and denoising algorithms are optimized for this quantitative metric. The performance in terms of subjective quality is typically not evaluated. Moreover, human visual system (HVS) is often not incorporated into denoising algorithm. This paper presents a new approach to color image denoising taking into consideration HVS model. The denoising process takes place in the wavelet transform domain. A Contrast Sensitivity Function (CSF) implementation is employed in the subband of wavelet domain based on an invariant single factor weighting and noise masking is adopted in succession. Significant improvement is reported in the experimental results in terms of perceptual error metrics and visual effect.     

利用数学形态学和方向窗的小波域双重局部维纳滤波图像去噪算法

基于小波的图像去噪算法是目前图像处理研究的一个热点。该文提出了一种结合椭圆型方向窗和数学形态学的小波域双重局部维纳滤波图像去噪算法。该算法同时利用了小波域子带的方向信息和图像本身所固有的几何结构:首先使用数学形态学把图像分成纹理区域和光滑区域两部分,然后结合椭圆型方向窗去估计小波域方向子带中每一点的信号方差,最后使用双重维纳滤波器对含噪图像进行去噪。实验结果表明该算法的去噪效果优于其它的采用二维可分离实小波进行图像去噪的算法。     

基于Hessian核范数正则化的快速图像复原算法

利用Hessian核范数进行图像复原是目前较好的高阶正则化方法,但是由于Hessian核范数正则项的高度非线性和不可微性,图像去模糊和去噪过程耦合度高,求解算法的复杂度高.本文利用变量分裂设计了一种具有闭解形式的交替迭代最小化快速图像复原算法,将图像去模糊、去噪分步进行,并给出算法的收敛性证明.实验结果表明,本文方法不仅在峰值信噪比方面优于原有的基于Hessian核范数图像复原的主优化(Majorization-Minimization,MM)方法,而且大大降低了算法的迭代次数和运行时间.     

An Objective Video Quality Metric for Compressed Stereoscopic Video

Stereoscopic video create the impression of depth by using two slightly different viewpoints. Conventional video quality assessment methods for 2D video are not suitable for stereoscopic video, so a new video quality assessment model for stereoscopic video is needed. In this paper, we propose a new objective video quality metric for compressed stereoscopic video. The proposed algorithm uses blocking artifacts, blurring in edge regions and video quality difference between two views. Blocking artifacts and blurring in edge regions are distortion appeared in the compressed video, and they are widely used in conventional video quality model. Difference in video quality between two views considers 3D effect of stereoscopic video. To verify the performance of the proposed algorithm, we performed subjective evaluation of stereoscopic video, and we confirmed that the proposed algorithm is superior to the conventional algorithms in respect to correlation with the subjective evaluation.     

基于奇异性检测的信号去噪新方法

该文引进了一种基于奇异性检测的信号去噪方法,并对其在二维降噪中所需进行的复杂的线性内插作了进一步简化,使得整个二维降噪得以大大简化而达到快速运算和节省存储量的目的。文中详细描述了该算法的理论基础并给出其一维计算机仿真,同时也给出了进一步简化后的二维降噪仿真。这种去噪方法不需要信号或噪声的先验信息。仿真结果表明,相比其它小波去噪方法,该方法的主要优势在于:它在某一时刻的脉冲噪声的辨识和去除能力相当强,而且在去噪的同时能很好地保持信号边缘。     

Image Sequence Denoising via Sparse and Redundant Representations

In this paper, we consider denoising of image sequences that are corrupted by zero-mean additive white Gaussian noise. Relative to single image denoising techniques, denoising of sequences aims to also utilize the temporal dimension. This assists in getting both faster algorithms and better output quality. This paper focuses on utilizing sparse and redundant representations for image sequence denoising, extending the work reported in , . In the single image setting, the K-SVD algorithm is used to train a sparsifying dictionary for the corrupted image. This paper generalizes the above algorithm by offering several extensions: i) the atoms used are 3-D; ii) the dictionary is propagated from one frame to the next, reducing the number of required iterations; and iii) averaging is done on patches in both spatial and temporal neighboring locations. These modifications lead to substantial benefits in complexity and denoising performance, compared to simply running the single image algorithm sequentially. The algorithm's performance is experimentally compared to several state-of-the-art algorithms, demonstrating comparable or favorable results.