自适应色彩矫正图像增强算法仿真研究

研究图像增强优化问题,由于大量的图像由于拍照抖动产生噪声,造成图像模糊问题,而传统的去除图像运动模糊的算法,具有计算复杂度过高和特定的假设条件的局限,为了改善图像视觉效果,提出了一种改进的计算量小的自适应矫正图像增强算法,利用模糊图像作为参考,对欠曝光图像进行非线性自适应色调矫正。首先利用非线性函数对不同通道颜色进行调节,然后使用自适应方法对亮度进行矫正仿真,得到最终清晰图像。仿真结果表明改算法可以有效增强图像,改善了图像的质量,具有一定的实际应用价值。     

Real-time motion blur using extruded triangles

Several algorithms have been introduced to render motion blur in real time by solving the visibility problem in the spatial-temporal domains. However, some algorithms render at interactive frame rates but have artifacts or noise. Therefore, we propose a new algorithm that renders real-time motion blur using extruded triangles. Our method uses two triangles in the previous frame and the current frame to make an extruded triangle then send it to rasterization. By using the standard rasterization, visibility determination is performed efficiently. To solve the occlusion between extruded triangles for a given pixel, we introduce a combination solution using a sorting in front-to-back order and bitwise operations in the spatial-temporal dimensions. This solution ensures that only non-occluded extruded triangles are shaded. We further improve performance of our algorithm using a coverage map.     

Motion blur identification in noisy images using mathematical models and statistical measures

Motion blur is one of the most common blurs that degrades images. Restoration of such images is highly dependent on estimation of motion blur parameters. Since 1976, many researchers have developed algorithms to estimate linear motion blur parameters. These algorithms are different in their performance, time complexity, precision and robustness in noisy environments. In this paper, we have presented a novel algorithm to estimate linear motion blur parameters such as direction and length. We used Radon transform to find direction and bispectrum modeling to find the length of motion. Our algorithm is based on the combination of spatial and frequency domain analysis. The great benefit of our algorithm is its robustness and precision in noisy images. We used statistical measures to prove goodness of our model. Our method was tested on 80 standard images that were degraded with different directions and motion lengths, with additive Gaussian noise. The error tolerance average of the estimated parameters was 0.9° in direction and 0.95 pixel in length and the standard deviations were 0.69 and 0.85, respectively.     

Motion‐adaptive alternate gamma drive for flicker‐free motion‐blur reduction in 100/120‐Hz LCD TV

Abstract— LCD motion blur is a well‐known phenomenon, and several approaches have been developed to address it. This includes very‐high‐performance approaches based on motion‐compensated frame rate conversion (MC‐FRC) and very‐low‐cost approaches based on impulsive driving. Impulsive‐driving schemes are attractive because of their low cost, but suffer from two significant issues — loss of luminance and large‐area flicker. A new impulsive‐driving approach using motion‐adaptive alternate gamma driving (MA‐AGD), which removes motion blur and preserves the original luminance level without causing large‐area flicker, is proposed.     

Non-uniform image deblurring using an optical computing system

Removing non-uniform blur caused by camera shaking is troublesome because of its high computational cost. We analyze the efficiency bottlenecks of a non-uniform deblurring algorithm and propose an efficient optical computation deblurring framework that implements the time-consuming and repeatedly required modules, i.e., non-uniform convolution and perspective warping, by light transportation. Specifically, the non-uniform convolution and perspective warping are optically computed by a hybrid system that is composed of an off-the-shelf projector and a camera mounted on a programmable motion platform. Benefitting from the high speed and parallelism of optical computation, our system has the potential to accelerate existing non-uniform motion deblurring algorithms significantly. To validate the effectiveness of the proposed approach, we also develop a prototype system that is incorporated into an iterative deblurring framework to effectively address the image blur of planar scenes that is caused by 3D camera rotation around the x-, y- and z-axes. The results show that the proposed approach has a high efficiency while obtaining a promising accuracy and has a high generalizability to more complex camera motions.     

仿人机器人视觉导航中的实时性运动模糊探测器设计

针对仿人机器人视觉导航系统的鲁棒性受到运动模糊制约的问题,提出一种基于运动模糊特征的实时性异常探测方法. 首先定量地分析运动模糊对视觉导航系统的负面影响,然后研究仿人机器人上图像的运动模糊规律,在此基础上对图像的运动模糊特征进行无参考的度量,随后采用无监督的异常探测技术,在探测框架下对时间序列上发生的图像运动模糊特征进行聚类分析,实时地召回数据流中的模糊异常,以增强机器人视觉导航系统对运动模糊的鲁棒性. 仿真实验和仿人机器人实验表明：针对国际公开的标准数据集和仿人机器人NAO数据集,方法具有良好的实时性（一次探测时间0.1s）和有效性（召回率98.5%,精确率90.7%）. 方法的探测框架对地面移动机器人亦具有较好的普适性和集成性,可方便地与视觉导航系统协同工作.     

Motion‐blur characterization on liquid‐crystal displays

Abstract— In this paper, several methods to characterize motion blur on liquid‐crystal displays are reviewed. Based on the assumptions of smooth‐pursuit eye tracking and one‐frame temporal luminance integration, a simple algorithm has been proposed to calculate the normalized blurred edge width (N‐BEW) and motion‐picture response time (MPRT) with a one‐frame‐time moving‐window function to LC temporal step response curves. A custom measurement system with a fast‐eye‐sensitivity‐compensated photodiode has been developed to characterize motion blur based on LC response curves (LCRCs). MPRT values obtained by using the algorithm mentioned above and those from the smooth‐pursuit‐camera methods agree. Perception experiments were conducted to validate the correspondence between the simulated results and actual perceived images by the human eyes. In addition, the insufficiency of MPRT to evaluate motion blur on impulse‐type light‐generation LCDs, by analyzing the measurement results of a scanning backlight LCD, is discussed.     

An edge detection framework conjoining with IMU data for assisting indoor navigation of visually impaired persons

Smartphone applications based on object detection techniques have recently been proposed to assist visually impaired persons with navigating indoor environments. In the smartphone, digital cameras are installed to detect objects which are important for navigation. Prior to detect the interested objects from images, edges on the objects have to be identified. Object edges are difficult to be detected accurately as the image is contaminated by strong image blur which is caused by camera movement. Although deblurring algorithms can be used to filter blur noise, they are computationally expensive and not suitable for real-time implementation. Also edge detection algorithms are mostly developed for stationary images without serious blur. In this paper, a modified sigmoid function (MSF) framework based on inertial measurement unit (IMU) is proposed to mitigate these problems. The IMU estimates blur levels to adapt the MSF which is computationally simple. When the camera is moving, the topological structure of the MSF is estimated continuously in order to improve effectiveness of edge detections. The performance of the MSF framework is evaluated by detecting object edges on video sequences associated with IMU data. The MSF framework is benchmarked against existing edge detection techniques and results show that it can obtain comparably lower errors. It is further shown that the computation time is significantly decreased compared to using techniques that deploy deblurring algorithms, thus making our proposed technique a strong candidate for reliable real-time navigation.     

Feature Detection Performance Based Benchmarking of Motion Deblurring Methods: Applications to Vision for Legged Robots

Dexterous legged robots can move on variable terrain at high speeds. The locomotion of these legged platforms on such terrain causes severe oscillations of the robot body depending on the surface and locomotion speed. Camera sensors mounted on such platforms experience the same disturbances, hence resulting in motion blur. This is a particular corruption of the image and results in information loss further resulting in degradation or loss of important image features. Although motion blur is a significant problem for legged mobile robots, it is of more general interest since it is present in many other handheld/mobile camera applications. Deblurring methods exist in the literature to compensate for blur, however most proposed performance metrics focus on the visual quality of compensated images. From the perspective of computer vision algorithms, feature detection performance is an essential factor that determines vision performance. In this study, we claim that existing image quality based metrics are not suitable to assess the performance of deblurring algorithms when the output is used for computer vision in general and legged robotics in particular. For comparatively evaluating deblurring algorithms, we define a novel performance metric based on the feature detection accuracy on sharp and deblurred images. We rank these algorithms according to the new metric as well as image quality based metrics from the literature and experimentally demonstrate that existing metrics may not be good indicators of algorithm performance, hence good selection criteria for computer vision application. Additionally, noting that a suitable data set to evaluate the effects of motion blur and its compensation for legged platforms is lacking in the literature, we develop a comprehensive multi-sensor data set for that purpose. The data set consists of monocular image sequences collected in synchronization with a low cost MEMS gyroscope, an accurate fiber optic gyroscope and an externally measured ground truth motion data. We make use of this data set for an extensive benchmarking of prominent motion deblurring methods from the literature in terms of existing and the proposed feature based metric.     

Fast Fully Data-Driven Image Restoration by means of Edge-Preserving Regularization

The fully data driven deconvolution of noisy images is a highly ill-posed problem, where the image, the blur and the noise parameters have to be simultaneously estimated from the data alone. Our approach is to exploit the information related to the image intensity edges both to improve the solution and to significantly reduce the computational costs. To detect reliable intensity edges, the image is modeled through a coupled Markov Random Field with an explicit, binary and constrained line process. Following a fully Bayesian approach, the solution should be given by the joint maximization of a distribution of the image field, the data, the blur and model parameters. A first, significant reduction in computational complexity is obtained by decomposing this joint maximization into a sequence of Maximum a posteriori and/or Maximum Likelihood estimations, to be performed alternately and iteratively. The presence of an explicit and binary line field is then exploited to reduce the computational cost of the usually very expensive model parameter estimation step. On this basis, we derive efficient and fast algorithms along with procedures which are feasible and effective for real-time applications, where the real-time requirements are not too strict. Indeed, the structure of these algorithms are intrinsically parallel, and thus suitable for implementation on high-performance machines, or on specialized hardware and allows the computation time to be greatly reduced. The experimental results show that the method allows one to obtain good blur estimates even in the presence of noise, without any need for smoothness assumptions on the blur coefficients, which would polarize the solution towards often unrealistic uniform blurs.     

Image motion estimation from motion smear-a new computational model

Motion smear is an important visual cue for motion perception by the human vision system (HVS). However, in image analysis research, exploiting motion smear has been largely ignored. Rather, motion smear is usually considered as a degradation of images that needs to be removed. In this paper, the authors establish a computational model that estimates image motion from motion smear information-“motion from smear”. In many real situations, the shutter of the sensing camera must be kept open long enough to produce images of adequate signal-to-noise ratio (SNR), resulting in significant motion smear in images. The authors present a new motion blur model and an algorithm that enables unique estimation of image motion. A prototype sensor system that exploits the new motion blur model has been built to acquire data for “motion-from-smear”. Experimental results on images with both simulated smear and real smear, using the authors' “motion-from-smear” algorithm as well as a conventional motion estimation technique, are provided. The authors also show that temporal aliasing does not affect “motion-from-smear” to the same degree as it does algorithms that use displacement as a cue. “Motion-from-smear” provides an additional tool for motion estimation and effectively complements the existing techniques when apparent motion smear is present     

倒谱和快速全变差去卷积的运动模糊图像复原

物体和成像系统之间的相对运动导致图像产生运动模糊,降低了图像质量,为了获得更加理想的复原图像,提出一种基于倒谱和快速全变差去卷积相结合的运动模糊图像复原算法。利用倒谱法对运动模糊图像的点扩散函数参数（模糊角度和模糊尺度）进行辩识,采用快速全变差去卷积法对模糊图像进行复原,采用多幅图像进行仿真实验测试算法的性能。仿真结果表明,相对于经典图像复原算法,该算法复原图像的主观视觉效果以及客观评价指标均更优,具有一定的实际利用价值。     

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.     

Improved scheme of estimating motion blur parameters for image restoration

A motion deblurring algorithm is proposed to enhance the quality of restoration based on the point spread function (PSF) identification in frequency spectrum. An improved blur angle identification algorithm characterized by bilateral-piecewise estimation strategy and the membership function method is presented by formulating the edges of the central bright stripe. Subsequently, the subpixel level image generated with bilinear interpolation is employed in the blur length estimation by calculating the distance between two adjacent dark strips. Through comparison with the existing algorithms, experimental results demonstrate that the proposed PSF estimation scheme could not only achieve higher accuracy for the blur angle and the blur length, but also produce more impressive restoration results. Furthermore, the robustness of our method is also validated in different noisy situations.     

Noncausal fractional directional differentiator and blind deconvoluation: motion blur estimation

The problem of blind estimation of motion blur parameters from a single image is addressed. The blur direction and extent of motion-blurred image, which are introduced by relative motion between a camera and its object scene, are needed in the methods of image restoration, such as blind deconvolution. As an extension to the fractional-order derivative, a noncausal fractional-order directional derivative operator is devised, which is robust to noise. Based on this new operator, a novel method identifying blur parameters is developed in this work. The performance comparison between the proposed method and the state-of-the-art method is also presented, demonstrating that the former provides better immunity to noise and capacity to identify motion blur extent, especially for large blur length.     

Interactive real-time motion blur

Motion blurring of fast-moving objects is highly desirable for virtual environments and 3D user interfaces. However, all currently known algorithms for generating motion blur are too slow for inclusion in interactive 3D applications. We introduce a new motion-blur algorithm that works in three dimensions on a per object basis. The algorithm operates in real time even for complex objects consisting of several thousand polygons. While it only approximates true motion blur, the generated results are smooth and visually consistent. We achieve this performance break-through by taking advantage of hardware-assisted rendering of semitransparent polygons, a feature commonly available in today's workstations.     

利用拉氏算子鉴别运动模糊方向

提出一种新的鉴别运动模糊图像的运动模糊方向的方法,它利用拉氏算子对运动模糊图像进行无方向性的二阶微分,并求微分图像的自相关,发现自相关图像中数值较大的点(鉴别点)能够有效标示出运动模糊方向。选取适当数目的候选鉴别点,并利用聚类方法剔除其中的奇异点,得到鉴别点;过零频尖峰(自相关图像的中心点)画一条直线,计算各个鉴别点到该直线的距离,求距离和;改变直线方向,当距离和最小时,直线的方向即为运动模糊方向。数据实验表明,这一新的运动模糊方向鉴别方法,具有抗噪声能力强、适用范围广、计算量小、鉴别精度高、稳定性好的优点．     

Characterizing displays by their temporal aperture: A theoretical framework

Abstract— The spatio‐temporal aperture and sample rate of a video display determines both the static and dynamic resolution of the video signal that is rendered. The dynamic display characteristics like the visibility of large‐area flicker, motion judder, and motion blur can be derived from the frame rate and the temporal extent of the pixel aperture (i.e., the temporal aperture). For example, liquid‐crystal displays (LCDs) have an aperture that is relatively small in the spatial dimension and wide in the temporal domain. Consequently, moving objects displayed on an LCD suffer from motion blur. Especially in TV applications, the temporal dimension has a large impact on the overall picture quality. The temporal aperture, together with the frame rate, is shown to predict the amount of perceived large‐area flicker, motion judder, and motion blur and also the performance of motion‐blur reduction algorithms for LCDs. From this analysis it is further determined how to obtain the optimal temporal aperture of a television display, for which not only properties of the human visual system (HVS), but also the properties of the video signal have to be taken into account.     

Video super-resolution using controlled subpixel detector shifts

Video cameras must produce images at a reasonable frame-rate and with a reasonable depth of field. These requirements impose fundamental physical limits on the spatial resolution of the image detector. As a result, current cameras produce videos with a very low resolution. The resolution of videos can be computationally enhanced by moving the camera and applying super-resolution reconstruction algorithms. However, a moving camera introduces motion blur, which limits super-resolution quality. We analyze this effect and derive a theoretical result showing that motion blur has a substantial degrading effect on the performance of super-resolution. The conclusion is that, in order to achieve the highest resolution motion blur should be avoided. Motion blur can be minimized by sampling the space-time volume of the video in a specific manner. We have developed a novel camera, called the "jitter camera," that achieves this sampling. By applying an adaptive super-resolution algorithm to the video produced by the jitter camera, we show that resolution can be notably enhanced for stationary or slowly moving objects, while it is improved slightly or left unchanged for objects with fast and complex motions. The end result is a video that has a significantly higher resolution than the captured one.