基于Gaussian-Hermite矩的旋转运动模糊不变量

目的 模糊图像的分析与识别是图像分析与识别领域的重要方向。有些图像形成过程中成像系统与物体之间存在相对旋转运动,如因导弹高速自旋转造成的制导图像的旋转运动模糊。大多数对于这类图像的识别都需要先对模糊图像进行“去模糊”的预处理,且该类方法存在计算时间复杂度较高及不适定的问题。对此,提出一种直接提取旋转运动模糊图像中的不变特征,用于旋转运动模糊图像目标检索和识别。方法 本文以旋转运动模糊的退化模型为出发点,提出了旋转运动模糊Gaussian-Hermite (GH)矩,构造了一组由5个对旋转变换和旋转运动模糊保持不变性的GH矩不变量组成的特征向量(rotational motion blur Gaussian-Hermite moment invariants,RMB_GHMI-5),可从旋转变换和旋转运动模糊的图像中直接进行目标检索和识别,无需前置复杂的“去模糊”预处理过程。结果 在USC-SIPI (University of Southern California — Signal and Image Processing Institute)数据集上进行不变性实验,对原图进行不同程度的旋转变换叠加旋转运动模糊处理,证明RMB_GHMI-5对于旋转变换和旋转运动模糊具有良好的稳定性和不变性。在两个数据集上与同类4种方法进行图像检索实验比较,在80%召回率下,本文方法维数更少,相比性能第2的特征向量,在Flavia数据集中,高斯噪声、椒盐噪声、泊松噪声和乘性噪声干扰下的准确率分别提高25.89%、39.95%、22.79%和35.80%;在Butterfly Image数据集中,高斯噪声、椒盐噪声、泊松噪声和乘性噪声干扰下的准确率分别提高4.79、7.63%、5.65%和18.31%。同时,在上述8个测试数据集中进行对比实验以验证融合算法的有效性,结果表明本文提出的GH矩和几何矩相融合算法显著改善了图像检索效果。结论 本文提出的RMB_GHMI-5特征向量在旋转变换和旋转运动模糊下具有良好的不变性与稳定性,在图像检索抗噪性能方面表现优异。相比同类方法,本文方法更具实际应用价值。     

Motion-Blurred Image Restoration Based on Joint Invertibility of PSFs

To implement restoration in a single motion blurred image, the PSF (Point Spread Function) is difficult to estimate and the image deconvolution is ill-posed as a result that a good recovery effect cannot be obtained. Considering that several different PSFs can get joint invertibility to make restoration well-posed, we proposed a motion-blurred image restoration method based on joint invertibility of PSFs by means of computational photography. Firstly, we designed a set of observation device which composed by multiple cameras with the same parameters to shoot the moving target in the same field of view continuously to obtain the target images with the same background. The target images have the same brightness, but different exposure time and different motion blur length. It is easy to estimate the blur PSFs of the target images make use of the sequence frames obtained by one camera. According to the motion blur superposition feature of the target and its background, the complete blurred target images can be extracted from the observed images respectively. Finally, for the same target images with different PSFs, the iterative restoration is solved by joint solution of multiple images in spatial domain. The experiments showed that the moving target observation device designed by this method had lower requirements on hardware conditions, and the observed images are more convenient to use joint-PSF solution for image restoration, and the restoration results maintained details well and had lower signal noise ratio (SNR).     

基于频谱预处理与改进霍夫变换的离焦模糊盲复原算法

为了提高离焦模糊图像复原清晰度,提出一种基于频谱预处理与改进霍夫变换的 离焦模糊盲复原算法。首先改进模糊图像频谱预处理策略,降低了噪声对零点暗圆检测的影响。 然后改进霍夫变换圆检测算法,在降低算法复杂度的同时,增强了模糊半径估计的准确性。最 后利用混合特性正则化复原图像模型对模糊图像进行迭代复原,使复原图像的边缘细节更加清 晰。实验结果表明,提出的模糊半径估计方法较其他方法平均误差更小,改进的频谱预处理策 略更有利于零点暗圆检测,改进的霍夫变换圆检测算法模糊半径估计精度更高,所提算法对已 知相机失焦的小型无人机拍摄的离焦模糊图像具有更好的复原效果。针对离焦模糊图像复原, 通过理论分析和实验验证了改进的模糊半径估计方法的鲁棒性强,所提算法的复原效果较好。     

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。     

图像模糊度参数估计与图像复原的实验及分析

由于成像系统与运动物体之间存在相对运动，摄像机在成象过程中经常会产生运动模糊。为了有效复原退化图像，需要从模糊图像中估计模糊函数的某些参数、噪声统计量等相关信息。在运动模糊中，模糊函数中最重要的参数是模糊度参数。模糊图像的模糊度内的像素点存在着相关性，利用其相关性可估计模糊度参数。根据运动模糊图像中沿着运动模糊方向与其他方向有不同图像特性的特点，估计水平直线运动图像模糊度参数并采用维纳滤波方法复原模糊图像，取得了较好的恢复效果。     

A Hybrid Motion Deblurring Strategy Using Patch Based Edge Restoration and Bilateral Filter

Motion blur is a common problem in digital photography. In the dim light, a long exposure time is needed to acquire a satisfactory photograph, and if the camera shakes during exposure, a motion blur is captured. Image deblurring has become a crucial image-processing challenge, because of the increased popularity of handheld cameras. Traditional motion deblurring methods assume that the blur degradation is shift-invariant; therefore, the deblurring problem can be reduced to a deconvolution problem. Edge-specific motion deblurring sharpened the strong edges of the image and then used them to estimate the blur kernel. However, this also enhanced noise and narrow edges, which cause ambiguity and ringing artifacts. We propose a hybrid-based single image motion deblurring algorithm to solve these problems. First, we separated the blurred image into strong edge parts and smooth parts. We applied the improved patch-based sharpening method to enhance the strong edge for kernel estimation, but for the smooth part, we used the bilateral filter to remove the narrow edge and the noise for avoiding the generation of ringing artifacts. Experimental results show that the proposed method is efficient at deblurring for a variety of images and can produce images of a quality comparable to other state-of-the-art techniques.     

Motion-based motion deblurring

Motion blur due to camera motion can significantly degrade the quality of an image. Since the path of the camera motion can be arbitrary, deblurring of motion blurred images is a hard problem. Previous methods to deal with this problem have included blind restoration of motion blurred images, optical correction using stabilized lenses, and special cmos sensors that limit the exposure time in the presence of motion. In this paper, we exploit the fundamental trade off between spatial resolution and temporal resolution to construct a hybrid camera that can measure its own motion during image integration. The acquired motion information is used to compute a point spread function (psf) that represents the path of the camera during integration. This psf is then used to deblur the image. To verify the feasibility of hybrid imaging for motion deblurring, we have implemented a prototype hybrid camera. This prototype system was evaluated in different indoor and outdoor scenes using long exposures and complex camera motion paths. The results show that, with minimal resources, hybrid imaging outperforms previous approaches to the motion blur problem. We conclude with a brief discussion on how our ideas can be extended beyond the case of global camera motion to the case where individual objects in the scene move with different velocities.     

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.     

利用残差密集网络的运动模糊复原方法

针对图像生成过程中由于物体运动或相机抖动产生的运动模糊问题,提出了利用残差密集网络的运动模糊图像复原方法。设计对抗网络结构,以残差密集网络为生成器,通过长短连接实现不同层次特征的融合,生成复原图像,以深度卷积网络为判别器,判断图像真伪,在生成器和判别器的对抗中提高网络性能;采用对抗损失和内容损失结合的损失函数,提高网络的复原效果;以端到端的方式,省略模糊核的估计过程,输入模糊图像直接获取复原图像。实验结果表明,该方法能够取得较好的复原效果。     

A Stochastic Approach for Blurred Image Restoration and Optical Flow Computation on Field Image Sequence

The blur in target images caused by camera vibration due to robot motion or hand shaking and by object(s) moving in the background scene is different to deal with in the computer vision system.In this paper,the authors study the relation model between motion and blur in the case of object motion existing in video image sequence,and work on a practical computation algorithm for both motion analysis and blut image restoration.Combining the general optical flow and stochastic process,the paper presents and approach by which the motion velocity can be calculated from blurred images.On the other hand,the blurred image can also be restored using the obtained motion information.For solving a problem with small motion limitation on the general optical flow computation,a multiresolution optical flow algoritm based on MAP estimation is proposed. For restoring the blurred image ,an iteration algorithm and the obtained motion velocity are used.The experiment shows that the proposed approach for both motion velocity computation and blurred image restoration works well.     

基于互相关模糊长度估计的图像复原

为提高含噪运动图像的复原质量,提出一种基于互相关原理的运动模糊长度估计算法。根据图像的Radon变换得到运动模糊角度,提取模糊中心各模糊路径上的像素灰度值,通过互相关计算确定模糊长度,采用Lucy-Richardson迭代算法对观测图像进行复原。实验结果表明,该算法的抗噪性较好,迭代收敛速度较快。     

一种运动模糊图像的模糊参数估计方法

在摄取图像的过程中,物体间的高速运动及景物与成像设备的相对运动是引发图像退化的主要原因之一,称之为运动模糊。模糊长度和模糊方向是运动模糊中影响图像模糊程度的主要参数。提出了一种改进的误差参数分析方法,对模糊长度进行估计并比较了不同的复原方法对参数误差曲线法估计的效果,同时提出运用傅里叶分解和Hough变换从模糊图像的频谱特性上对运动模糊方向进行了估计。实验结果表明,所提出的方法对运动模糊图像的复原有良好的效果。     

Motion tracking with an active camera

This paper describes a method for real-time motion detection using an active camera mounted on a pan/tilt platform. Image mapping is used to align images of different viewpoints so that static camera motion detection can be applied. In the presence of camera position noise, the image mapping is inexact and compensation techniques fail. The use of morphological filtering of motion images is explored to desensitize the detection algorithm to inaccuracies in background compensation, Two motion detection techniques are examined, and experiments to verify the methods are presented. The system successfully extracts moving edges from dynamic images even when the pan/tilt angles between successive frames are as large as 3     

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.     

基于零边界像素的模糊图像复原模型

提出一种基于零边界像素的模糊图像复原模型。通过分析水平运动模糊的成因,以矩阵形式建立运动模糊的一般模糊模型,利用广义逆矩阵方法对模糊图像进行复原,使用零边界像素条件完善复原模型,以减少周期干扰。实验结果表明,与维纳滤波法相比,该模型在不考虑噪声的情况下,图像复原效果更好。     

Recognition of camera-captured low-quality characters using motion blur information

Camera-based character recognition has gained attention with the growing use of camera-equipped portable devices. One of the most challenging problems in recognizing characters with hand-held cameras is that captured images undergo motion blur due to the vibration of the hand. Since it is difficult to remove the motion blur from small characters via image restoration, we propose a recognition method without de-blurring. The proposed method includes a generative learning method in the training step to simulate blurred images by controlling blur parameters. The method consists of two steps. The first step recognizes the blurred characters based on the subspace method, and the second one reclassifies structurally similar characters using blur parameters estimated from the camera motion. We have experimentally proved that the effective use of motion blur improves the recognition accuracy of camera-captured characters.     

Motion Deblurring from a Single Image using Circular Sensor Motion

Image blur caused by object motion attenuates high frequency content of images, making post‐capture deblurring an ill‐posed problem. The recoverable frequency band quickly becomes narrower for faster object motion as high frequencies are severely attenuated and virtually lost. This paper proposes to translate a camera sensor circularly about the optical axis during exposure, so that high frequencies can be preserved for a wide range of in‐plane linear object motion in any direction within some predetermined speed. That is, although no object may be photographed sharply at capture time, differently moving objects captured in a single image can be deconvolved with similar quality. In addition, circular sensor motion is shown to facilitate blur estimation thanks to distinct frequency zero patterns of the resulting motion blur point‐spread functions. An analysis of the frequency characteristics of circular sensor motion in relation to linear object motion is presented, along with deconvolution results for photographs captured with a prototype camera.     

A psychophysical study of improvements in motion‐image quality by using high frame rates

Abstract— The ideal frame rate for the highest motion‐image quality with respect to blur and jerkiness is presented. In order to determine the requirements for avoiding these impairments, motion images from a high‐speed camera and computer graphics were combined with a high‐speed display to perform a psychophysical evaluation. The camera, operating at 1000 fps, and image processing were used to simulate various frame rates and shutter speeds, and a 480‐Hz CRT display was used to present motion images simulating various frame rates and time characteristics of the display. Subjects were asked to evaluate the difference in quality between motion images at various frame rates. A frame rate of 480 fps was chosen to be an appropriate reference frame rate that, as a first estimation, enables coverage up to the human‐dynamic‐resolution (HDR) limit based on another experiment using real moving charts. The results show that a frame rate of 120 fps provides good improvement compared to that of 60 fps, and that the maximum improvement beyond which evaluation is saturated is found at about 240 fps for representative standard‐resolution natural images.     

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。     

模糊影响感知的交互设计

图像的模糊变化会影响人类视觉对图像的感知。在特定条件下,它能提升人类感知和识别图像的能力。在摄像过程中,图像的清晰度或模糊度是由拍摄相机的参数决定的,需要较为专业的摄像器材,成本太高。论文构造一个图像模糊区域生成交互系统,以便于普通用户生成模糊区域达到特殊效果。具体表现为利用模糊算法,构建相机成像模型,通过操纵成像参数,达到对模糊有效操控,实现交互目的。交互平台内容主要包括,在图像上交互地生成一个与场景中一些平行线方向一致的网格,通过预设参数求出图像特定区域中的每个像素成像后的模糊量,最后合成模糊图像。在交互平台进行了实验,结果验证了算法和交互平台的有效性。