一种统一的硬件加速自适应EWA Splatting算法

提出了一种新的硬件加速自适应EWA(椭圆加权平均)Splatting算法框架,可同时适用于三维体数据和点模型.算法将高斯重建核与低通图像滤波核结合,得到反走样、无模糊的高质量图像.提出一种高效的白适应滤波方法,减少了高质量EWA Splatting的计算量.提出了自适应体EWA Splatting的3种数据存储模式和一系列高级特性,其中包括交互式分类、体一面混合绘制策略和自适应浮点累加.展示了如何在可编程图形处理单元(GPU)中计算体数据和点模型数据的EWA Splat基元.实验表明,文中的方法在一台普通微机上每秒可绘制1500万～2000万个基元,达到较高的图像质量与交互的绘制速度.     

基于频域识别运动模糊的模糊方向和模糊度

运动模糊图片的频谱中存在平行的零点直线。这里提出了一种算法,识别零点直线含有的运动模糊方向和模糊度信息:首先对模糊图像进行分块,然后将分成的小块相加,利用拉氏算子对最后的相加结果进行无方向性的二阶微分,通过二维FFT变换求得频谱。接着对频谱取绝对值、做归一化之后,修饰二值化结果,最后进行Radon变换,求得点扩展函数的运动方向和模糊长度。实验论证该算法可靠通用,性能优良。     

Algorithm for the Removing Uniformed Motion Blur

Motion deblurring is one of the basic problems inthe field of image processing. This paper summarizes the mathematical basis of the previous work and presents a deblurringmethod that can improve the estimation of the motion blurkernel and obtain a better result than the traditional methods.Experiments show the motion blur kernel loses some important and useful properties during the estimation of the kernel which may cause a bad estimation and increase the ringingartifacts. Considering that the kernel is provided by the motion of the imaging sensor during the exposure and that the kernel shows the trace of the motion, this paper ensures the physical meaning of the kernel such as the continuity and the center of thekernel during the iterative process. By adding a post process to the estimation of the motion blur kernel, we remove some discrete points and make use of the centralizationof the kernel in order to accurate the estimation. The experiment shows the existence of the post process improves the effect of the estimation of the kernel and provides a better result with the clear edges.     

空间尺度信息的运动模糊核估计方法

运动模糊核的准确估计是实现单幅运动模糊图像盲复原成功的关键.但是,因为不能准确提取出有利的图像边缘以及简单的正则化约束项的设计,导致现有运动模糊核（motion blur kernel,简称MBK）的估计并不十分准确,存在瑕疵.因此,为了能够估计出准确的运动模糊核,提出了一种基于空间尺度信息的运动模糊核估计方法.首先,为了准确地提取有利的图像边缘,移除有害的图像结构,提出了一种基于图像空间尺度信息的图像平滑模型,实现有利图像边缘的准确快速提取;然后,从运动模糊核的内在特性出发,将空间域的L₀范数和梯度域的L₂范数结合到一起,提出了一种正则化约束模型,很好地保证了运动模糊核的稀疏平滑特性,并结合之前提取出的有利的图像边缘,共同实现运动模糊核的准确估计;最后,采用一种半二次性分裂的交互式最优化策略对提出的模型进行最优化求解.在客观的评价指标和主观的视觉效果上进行了大量实验,其结果证明所提出的方法能够估计出更准确的MBK和复原出更高质量的去模糊图像.     

Layered Reconstruction for Defocus and Motion Blur

Light field reconstruction algorithms can substantially decrease the noise in stochastically rendered images. Recent algorithms for defocus blur alone are both fast and accurate. However, motion blur is a considerably more complex type of camera effect, and as a consequence, current algorithms are either slow or too imprecise to use in high quality rendering. We extend previous work on real‐time light field reconstruction for defocus blur to handle the case of simultaneous defocus and motion blur. By carefully introducing a few approximations, we derive a very efficient sheared reconstruction filter, which produces high quality images even for a low number of input samples. Our algorithm is temporally robust, and is about two orders of magnitude faster than previous work, making it suitable for both real‐time rendering and as a post‐processing pass for offline rendering.     

Optimized Sub-Sampling of Point Sets for Surface Splatting

Using surface splats as a rendering primitive has gained increasing attention recently due to its potential for high‐performance and high‐quality rendering of complex geometric models. However, as with any other rendering primitive, the processing costs are still proportional to the number of primitives that we use to represent a given object. This is why complexity reduction for point‐sampled geometry is as important as it is, e.g., for triangle meshes. In this paper we present a new sub‐sampling technique for dense point clouds which is specifically adjusted to the particular geometric properties of circular or elliptical surface splats. A global optimization scheme computes an approximately minimal set of splats that covers the entire surface while staying below a globally prescribed maximum error toleranceε. Since our algorithm converts pure point sample data into surface splats with normal vectors and spatial extent, it can also be considered as a surface reconstruction technique which generates a hole‐free piecewise linearC^?1 continuous approximation of the input data. Here we can exploit the higher flexibility of surface splats compared to triangle meshes. Compared to previous work in this area we are able to obtain significantly lower splat numbers for a given error tolerance.     

Controlling Motion Blur in Synthetic Long Time Exposures

In a photo, motion blur can be used as an artistic style to convey motion and to direct attention. In panning or tracking shots, a moving object of interest is followed by the camera during a relatively long exposure. The goal is to get a blurred background while keeping the object sharp. Unfortunately, it can be difficult to impossible to precisely follow the object. Often, many attempts or specialized physical setups are needed. This paper presents a novel approach to create such images. For capturing, the user is only required to take a casually recorded hand‐held video that roughly follows the object. Our algorithm then produces a single image which simulates a stabilized long time exposure. This is achieved by first warping all frames such that the object of interest is aligned to a reference frame. Then, optical flow based frame interpolation is used to reduce ghosting artifacts from temporal undersampling. Finally, the frames are averaged to create the result. As our method avoids segmentation and requires little to no user interaction, even challenging sequences can be processed successfully. In addition, artistic control is available in a number of ways. The effect can also be applied to create videos with an exaggerated motion blur. Results are compared with previous methods and ground truth simulations. The effectiveness of our method is demonstrated by applying it to hundreds of datasets. The most interesting results are shown in the paper and in the supplemental material.     

Motion Blur Rendering: State of the Art

Motion blur is a fundamental cue in the perception of objects in motion. This phenomenon manifests as a visible trail along the trajectory of the object and is the result of the combination of relative motion and light integration taking place in film and electronic cameras. In this work, we analyse the mechanisms that produce motion blur in recording devices and the methods that can simulate it in computer generated images. Light integration over time is one of the most expensive processes to simulate in high‐quality renders, as such, we make an in‐depth review of the existing algorithms and we categorize them in the context of a formal model that highlights their differences, strengths and limitations. We finalize this report proposing a number of alternative classifications that will help the reader identify the best technique for a particular scenario.     

基于Bottom-hat频谱方法的运动模糊参数估计方法

运动模糊是由于物体与成像系统间的相对运动而形成的,匀速直线模糊图像的复原需要得到精确的模糊参数。根据理论,匀速直线运动模糊的模糊参数与频谱中的零点有关,其频谱暗线数目与模糊尺度有关,且频谱暗线与角度垂直。在检测频谱暗线时,由于受到图像结构或者噪声影响,难以准确定位频谱暗线;同时,图像长宽比的不同也会影响频谱结构。针对以上问题,文中利用形态学中的Bottom-hat操作对模糊图像频谱进行处理,并对处理后的频谱采用霍夫变换得到模糊角度,再利用模糊角度和中心零点距离得出模糊尺度。实验结果表明,该方法检测模糊尺度的误差小于0.25个像素,角度小于0.6°,且拥有较好的稳定性,可以正确检测不同尺度和内容模糊图像的模糊参数。     

Adaptable Splatting for Irregular Volume Rendering

By employment of a footprint table in conducting intensity integration, splatting method has been very successful in rendering regular data volumes. Recently, the method has also been extended to render irregular data volumes. However, since samples in irregular volumes vary greatly in size and shape, the footprint table is unable to be employed in an efficient manner. This hinders the application of splatting approach from being used in the irregular volume case. In this paper, an adaptable splatting method is proposed, which provides an efficient way to integrate color intensity in terms of footprint table for the samples in various sizes. Experiments show that the new method may be used to produce better images without extra expense.     

基于方向微分的运动模糊方向鉴别

在曝光的瞬间，造成图像模糊的被摄景物与相机的相对运动，虽可近似作为直线运动来处理，但模糊图像中的运动模糊方向未知。若能由模糊图像出发，估计出运动模糊方向，则可以通过图像旋转将运动模糊方向旋转到水平轴，这样图像恢复就可由2维问题转化为1维问题，这就大大降低了图像恢复的难度，并为图像恢复的并行计算创造了有利条件。为实现这一目的，将原图像看作是各向同性的一阶马尔科夫过程，提出了一种新的基于方向微分的运动模糊方向鉴别方法，该方法不仅可以高精度鉴别匀速运动、加速运动、振动等各种运动的模糊方向，而且具有鉴别范围大、鉴别精度高、稳定性好的优点。另外，为了具体实现这种鉴别，还给出了采用双线性插值或三次C样条插值进行方向鉴别的详细计算方法，其中双线性插值方法计算量小，但三次C样条插值方法的鉴别精度比双线性插值方法高，而且通过加权平均，还可进一步降低各种随机因素引起的鉴别误差，这不仅提高了鉴别精度，而且增强了运动模糊方向鉴别的稳定性，因此能够更加有效地进行方向鉴别。     

改进多尺度CNN网络的运动模糊图像复原算法

针对多尺度CNN网络编码过程中存在获取特征信息不足,导致重建的去运动模糊图像质量不佳。该研究提出了一种将明暗通道先验嵌入多尺度网络,并在网络中引入双重注意力机制的解决方法,该方法增强了网络对先验信息的获取能力,加强重点信息获取的同时提高动态去模糊效果。该方法与同类方法相比峰值信噪比(PSNR)和结构相似度(SSIM)均获得了提升。     

基于小波变换的运动模糊图像融合研究

由于拍摄目标和成像设备之间的相对运动,往往造成获取的图像模糊.为了消除运动模糊和提高图像恢复质量,利用双线性插值和方向微分算子,采用”先粗后细”的方法,即先从退化图像中粗略确定运动模糊方向范围,然后高精度确定其具体数值.这种方法具有计算量小,精度高,稳定性好,适用范围广的优点.由于模糊图像的像素点之间的高度相关性,采用差分和自相关技术精确确定模糊尺度.由运动模糊方向和模糊尺度构造点扩展函数,然后利用不同的滤波方法得到恢复图像,采用不同的图像融合方法对恢复图像进行融合实验.实验结果表明,基于小波变换的运动模糊图像的融合方法得到的融合图像取得了较好的客观评价和主观视觉效果,具有一定的实用价值.     

视频透视增强现实系统中的运动模糊研究

针对Radon变换对增强现实运动模糊中短模糊尺度无效的问题,提出一种将Radon变换和Canny边缘检测相结合,并基于视频透视的虚拟物体运动模糊模拟算法。采用Canny边缘检测和Radon变换方法,获得点扩散函数参数和基于GPU实现运动模糊的实时渲染。实验结果证明,该算法在视频透视增强现实系统中,对于长短模糊尺度都能较好地模拟运动模糊效果,从而实现虚实场景的无缝融合。     

无人机侦察图像运动模糊复原方法研究

无人机具有体积小、使用方便以及战场生存能力较强等优点,在军事领域中应用越来越广泛。无人机进行情报侦察时,成像设备与拍摄物之间存在相对运动,使得拍摄到的图像出现模糊,严重影响后期的情报处理,因此如何提高模糊图片的质量成为无人机情报处理中的重要课题。介绍了在无人机侦察这一背景下图像运动模糊的降质机理及成像特点,对运动模糊复原领域中图像非盲复原与盲复原两类方法进行研究,从算法原理和实际应用两方面对每种算法的优缺点进行了总结。最后对无人机侦察图像运动模糊复原算法的发展趋势做出展望,实时性、适用性与智能性将成为算法的主流发展方向。     

EWA splatting

We present a framework for high quality splatting based on elliptical Gaussian kernels. To avoid aliasing artifacts, we introduce the concept of a resampling filter, combining a reconstruction kernel with a low-pass filter. Because of the similarity to Heckbert's (1989) EWA (elliptical weighted average) filter for texture mapping, we call our technique EWA splatting. Our framework allows us to derive EWA splat primitives for volume data and for point-sampled surface data. It provides high image quality without aliasing artifacts or excessive blurring for volume data and, additionally, features anisotropic texture filtering for point-sampled surfaces. It also handles nonspherical volume kernels efficiently; hence, it is suitable for regular, rectilinear, and irregular volume datasets. Moreover, our framework introduces a novel approach to compute the footprint function, facilitating efficient perspective projection of arbitrary elliptical kernels at very little additional cost. Finally, we show that EWA volume reconstruction kernels can be reduced to surface reconstruction kernels. This makes our splat primitive universal in rendering surface and volume data.     

Photon Differential Splatting for Rendering Caustics

We present a photon splatting technique which reduces noise and blur in the rendering of caustics. Blurring of illumination edges is an inherent problem in photon splatting, as each photon is unaware of its neighbours when being splatted. This means that the splat size is usually based on heuristics rather than knowledge of the local flux density. We use photon differentials to determine the size and shape of the splats such that we achieve adaptive anisotropic flux density estimation in photon splatting. As compared to previous work that uses photon differentials, we present the first method where no photons or beams or differentials need to be stored in a map. We also present improvements in the theory of photon differentials, which give more accurate results and a faster implementation. Our technique has good potential for GPU acceleration, and we limit the number of parameters requiring user adjustment to an overall smoothing parameter and the number of photons to be traced.