Reinterpretable Imager: Towards Variable Post‐Capture Space,Angle and Time Resolution in Photography

We describe a novel multiplexing approach to achieve tradeoffs in space, angle and time resolution in photography. We explore the problem of mapping useful subsets of time‐varying 4D lightfields in a single snapshot. Our design is based on using a dynamic mask in the aperture and a static mask close to the sensor. The key idea is to exploit scene‐specific redundancy along spatial, angular and temporal dimensions and to provide a programmable or variable resolution tradeoff among these dimensions. This allows a user to reinterpret the single captured photo as either a high spatial resolution image, a refocusable image stack or a video for different parts of the scene in post‐processing. A lightfield camera or a video camera forces a‐priori choice in space‐angle‐time resolution. We demonstrate a single prototype which provides flexible post‐capture abilities not possible using either a single‐shot lightfield camera or a multi‐frame video camera. We show several novel results including digital refocusing on objects moving in depth and capturing multiple facial expressions in a single photo.     

Convolutional Sparse Coding for Capturing High‐Speed Video Content

Video capture is limited by the trade‐off between spatial and temporal resolution: when capturing videos of high temporal resolution, the spatial resolution decreases due to bandwidth limitations in the capture system. Achieving both high spatial and temporal resolution is only possible with highly specialized and very expensive hardware, and even then the same basic trade‐off remains. The recent introduction of compressive sensing and sparse reconstruction techniques allows for the capture of single‐shot high‐speed video, by coding the temporal information in a single frame, and then reconstructing the full video sequence from this single‐coded image and a trained dictionary of image patches. In this paper, we first analyse this approach, and find insights that help improve the quality of the reconstructed videos. We then introduce a novel technique, based on convolutional sparse coding (CSC), and show how it outperforms the state‐of‐the‐art, patch‐based approach in terms of flexibility and efficiency, due to the convolutional nature of its filter banks. The key idea for CSC high‐speed video acquisition is extending the basic formulation by imposing an additional constraint in the temporal dimension, which enforces sparsity of the first‐order derivatives over time.     

Coded exposure HDR light‐field video recording

Capturing exposure sequences to compute high dynamic range (HDR) images causes motion blur in cases of camera movement. This also applies to light‐field cameras: frames rendered from multiple blurred HDR light‐field perspectives are also blurred. While the recording times of exposure sequences cannot be reduced for a single‐sensor camera, we demonstrate how this can be achieved for a camera array. Thus, we decrease capturing time and reduce motion blur for HDR light‐field video recording. Applying a spatio‐temporal exposure pattern while capturing frames with a camera array reduces the overall recording time and enables the estimation of camera movement within one light‐field video frame. By estimating depth maps and local point spread functions (PSFs) from multiple perspectives with the same exposure, regional motion deblurring can be supported. Missing exposures at various perspectives are then interpolated.     

Acquisition of High Spatial and Spectral Resolution Video with a Hybrid Camera System

We present a hybrid camera system for capturing video at high spatial and spectral resolutions. Composed of an red, green, and blue (RGB) video camera, a grayscale video camera and a few optical elements, the hybrid camera system simultaneously records two video streams: an RGB video with high spatial resolution, and a multispectral (MS) video with low spatial resolution. After registration of the two video streams, our system propagates the MS information into the RGB video to produce a video with both high spectral and spatial resolution. This propagation between videos is guided by color similarity of pixels in the spectral domain, proximity in the spatial domain, and the consistent color of each scene point in the temporal domain. The propagation algorithm, based on trilateral filtering, is designed to rapidly generate output video from the captured data at frame rates fast enough for real-time video analysis tasks such as tracking and surveillance. We evaluate the proposed system using both simulations with ground truth data and on real-world scenes. The accuracy of spectral capture is examined through comparisons with ground truth and with a commercial spectrometer. The utility of this high resolution MS video data is demonstrated on the applications of dynamic white balance adjustment, object tracking, and separating the appearance contributions of different illumination sources. The various high resolution MS video datasets that we captured will be made publicly available to facilitate research on dynamic spectral data analysis.     

Moving vehicle detection,tracking and traffic parameter estimation from a satellite video: a perspective on a smarter city

ABSTRACT

Satellite remote sensing is undergoing a revolution in terms of sensors and temporal coverage. The possibility of acquiring earth’s surface video from space provides an opportunity to investigate broader applications of remote sensing. High-resolution spaceborne videos can become a vital factor in earth observation. Temporally continuous tracking of moving objects, i.e. vehicles, vessels, or even military equipment on Earth’s surface demands high spatial resolution satellite videos. Detecting moving vehicles in the urban areas from space video can lead governments to a new era of traffic monitoring. Satellite videos will find many applications in the field of traffic monitoring. In this article, first, moving vehicles are detected using background subtraction with 94.7% accuracy. Afterwards, vehicles’ trajectories, average velocities, dynamic velocities, and space-time diagram are estimated and trajectories are classified based on velocities. Finally, the total frame traffic density is computed.     

基于自适应码率分配的压缩传感深度视频编码方法

压缩传感深度视频(CSDV)是由深度视频经过压缩得到,它的冗余信息仍然巨大,由此,文中提出基于高斯混合模型和边缘码率分配的深度视频编码方法.在时域方向上,使用压缩传感,压缩八帧深度视频,得到一帧CSDV图像.为了减小量化的计算复杂度,将一帧CSDV图像分割成一系列大小相同且互不重合的视频块,使用Canny算子作为边界提取工具提取视频块的边界.根据每个视频块中非零像素所占的百分比,给不同的视频块分配不同的比特数.在模型中,使用高斯混合模型建模这些视频块,用于设计乘积矢量量化器,再使用乘积矢量量化器量化这些视频块.     

基于压缩感知的视频双水印算法研究

针对数字视频的内容保护与帧内、帧间篡改检测的难题,采用压缩感知理论提取视频的内容特征作为水印,提出一种双水印的视频保护和篡改检测算法。首先,利用压缩感知过程提取I帧宏块的内容特征,生成半脆弱的内容认证水印;然后,对帧序号进行二值运算,生成完整性水印;最后,利用压缩感知信号重构OMP(Orthogonal Matching Pursuit)算法把生成的双水印嵌入到I帧和P帧相应宏块的DCT高频系数的压缩测量值中,以此提高视频水印的抗攻击能力,并实现对视频篡改的检测。仿真实验表明,所提算法对视频帧内篡改具有精确定位到子块的检测能力;同时对帧插入、帧删除、帧交换等类型的视频帧间篡改具有很强的检测能力。     

Distributed compressed video sensing based on key frame secondary reconstruction

Distributed compressed video sensing scheme combines advantages of compressive sensing and distributed video coding to get better performance, in the meantime, adapts to the limited-resource wireless multimedia sensor network. However, in the conventional distributed compressed video sensing schemes, self-similarity and high sampling rate of the key frame have not been sufficiently utilized, and the overall computational complexity increases with the development of these schemes. To solve the aforementioned problems, we propose a novel distributed compressed video sensing scheme. A new key frame secondary reconstruction scheme is proposed, which further improves the quality of key frame and decreases computational complexity. The key frame’s initial reconstruction value is deeply exploited to assist the key frame secondary reconstruction. Then, a hypotheses set acquisition algorithm based on motion estimation is proposed to improve the quality of hypotheses set by optimizing the searching window under low complexity. Experimental results demonstrate that the overall performance of the proposed scheme outperforms that of the state-of-the-art methods.     

Multisampling Compressive Video Spectroscopy

The coded aperture snapshot spectral imaging (CASSI) architecture has been employed widely for capturing hyperspectral video. Despite allowing concurrent capture of hyperspectral video, spatial modulation in CASSI sacrifices image resolution significantly while reconstructing spectral projection via sparse sampling. Several multiview alternatives have been proposed to handle this low spatial resolution problem and improve measurement accuracy, for instance, by adding a translation stage for the coded aperture or changing the static coded aperture with a digital micromirror device for dynamic modulation. State‐of‐the‐art solutions enhance spatial resolution significantly but are incapable of capturing video using CASSI. In this paper, we present a novel compressive coded aperture imaging design that increases spatial resolution while capturing 4D hyperspectral video of dynamic scenes. We revise the traditional CASSI design to allow for multiple sampling of the randomness of spatial modulation in a single frame. We demonstrate that our compressive video spectroscopy approach yields enhanced spatial resolution and consistent measurements, compared with the traditional CASSI design.     

Outdoor Human Motion Capture by Simultaneous Optimization of Pose and Camera Parameters

We present a method for capturing the skeletal motions of humans using a sparse set of potentially moving cameras in an uncontrolled environment. Our approach is able to track multiple people even in front of cluttered and non‐static backgrounds, and unsynchronized cameras with varying image quality and frame rate. We completely rely on optical information and do not make use of additional sensor information (e.g. depth images or inertial sensors). Our algorithm simultaneously reconstructs the skeletal pose parameters of multiple performers and the motion of each camera. This is facilitated by a new energy functional that captures the alignment of the model and the camera positions with the input videos in an analytic way. The approach can be adopted in many practical applications to replace the complex and expensive motion capture studios with few consumer‐grade cameras even in uncontrolled outdoor scenes. We demonstrate this based on challenging multi‐view video sequences that are captured with unsynchronized and moving (e.g. mobile‐phone or GoPro) cameras.     

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.     

Human-vision-based real-time stereopsis

The progression in the field of stereoscopic imaging has resulted in impressive 3D videos. This technology is now used for commercial and entertainment purposes and sometimes even for medical applications. Currently, it is impossible to produce quality anaglyph video using a single camera under different moving and atmospheric conditions with the corresponding depth, local colour, and structural information. The proposed study challenges the previous researches by introducing single camera based method for anaglyph reconstruction and it mainly concentrates on human visual perception, where as the previous methods used dual camera, depth sensor, multi view, which demand not only long duration they also suffer from photometric distortion due to variation in angular alignment. This study also contributes clear individual image without any occlusion with another image. We use an approach based on human vision to determine the corresponding depth information. The source frames are shifted slightly in opposite directions as the distance between the pupils increases. We integrate the colour components of the shifted frames to generate contrasting colours for each one of the marginally shifted frames. The colour component images are then reconstructed as a cyclopean image. We show the results of our method by applying it to quickly varying video sequences and compare its performance to other existing methods.     

Video flickering removal using temporal reconstruction optimization

In this paper, we introduce an approach to remove the flickers in the videos, and the flickers are caused by applying image-based processing methods to original videos frame by frame. First, we propose a multi-frame based video flicker removal method. We utilize multiple temporally corresponding frames to reconstruct the flickering frame. Compared with traditional methods, which reconstruct the flickering frame just from an adjacent frame, reconstruction with multiple temporally corresponding frames reduces the warp inaccuracy. Then, we optimize our video flickering method from following aspects. On the one hand, we detect the flickering frames in the video sequence with temporal consistency metrics, and just reconstructing the flickering frames can accelerate the algorithm greatly. On the other hand, we just choose the previous temporally corresponding frames to reconstruct the output frames. We also accelerate our video flicker removal with GPU. Qualitative experimental results demonstrate the efficiency of our proposed video flicker method. With algorithmic optimization and GPU acceleration, the time complexity of our method also outperforms traditional video temporal coherence methods.

     

Hybrid light field imaging for improved spatial resolution and depth range

Light field imaging involves capturing both angular and spatial distribution of light; it enables new capabilities, such as post-capture digital refocusing, camera aperture adjustment, perspective shift, and depth estimation. Micro-lens array (MLA)-based light field cameras provide a cost-effective approach to light field imaging. There are two main limitations of MLA-based light field cameras: low spatial resolution and narrow baseline. While low spatial resolution limits the general purpose use and applicability of light field cameras, narrow baseline limits the depth estimation range and accuracy. In this paper, we present a hybrid stereo imaging system that includes a light field camera and a regular camera. The hybrid system addresses both spatial resolution and narrow baseline issues of the MLA-based light field cameras while preserving light field imaging capabilities.     

残差分布式视频压缩感知*

压缩感知(CS)是一种能同时进行数据采集和压缩的新理论,为简化编码算法提供了依据,同时,分布式视频编码(DVC)为低复杂度的视频编码提供了思路。因此,通过整合DVC和CS各自的特性以构建编码简单的视频编码框架,并采用残差技术来提高系统性能,最终提出了一种残差分布式视频压缩感知(RDCVS)算法:对关键帧进行传统的帧内编、解码;而对非关键帧,编码端采用一种基于残差联合稀疏模型的随机观测,解码端利用边信息和改进的梯度投影重建(GPSR)算法进行优化重构。由于将运动估计和变换编码等复杂度较高的运算转移到解码端进行,因而RDCVS保持了低复杂度的编码特性。实验结果表明,RDCVS算法比参考方案的恢复质量提高了2～3 dB。     

面向上行流媒体的压缩感知视频流技术前沿

上行流媒体在军民融合领域展现出日益重要的新兴战略价值,压缩感知视频流技术体系在上行流媒体应用中具有前端功耗低、容错性好、适用信号广等独特优势,已成为当前可视通信研究的前沿与热点之一。本文从阐述上行流媒体的应用特征出发,从性能指标、并行分块计算成像、低复杂度视频编码、视频重构和语义质量评价等方面,分析了当前针对压缩感知视频流的基础理论与关键技术,对国内外相关的研究进展进行了探究与比较。面向上行流媒体的压缩感知视频流面临着观测效率难控、码流适配困难和重建质量较低等技术挑战。对压缩感知视频流的技术发展趋势进行展望,未来将通过前端与智能云端的分工协作,突破高效率的视频观测与语义质量导引视频重构等关键技术,进一步开拓压缩感知视频流在上行流媒体应用中的定量优势与演进途径。     

Real-Time Omnidirectional Image Sensors

Conventional T.V. cameras are limited in their field of view. A real-time omnidirectional camera which can acquire an omnidirectional (360 degrees) field of view at video rate and which could be applied in a variety of fields, such as autonomous navigation, telepresence, virtual reality and remote monitoring, is presented. We have developed three different types of omnidirectional image sensors, and two different types of multiple-image sensing systems which consist of an omnidirectional image sensor and binocular vision. In this paper, we describe the outlines and fundamental optics of our developed sensors and show examples of applications for robot navigation.     

基于自适应字典学习的动态磁共振并行重建

动态磁共振成像技术在时空扫描精度上不能兼顾,是目前医学界的一个难点。动态磁共振成像数据在时空域具有很强的稀疏特性,使得压缩感知技术被广泛应用于MR图像重建。提出一种基于压缩感知自适应字典学习的动态磁共振并行重建方法,以高精采样的第一帧作为参考,实现对任意n个相邻帧的dMRI图像子序列的实时并行重建。与目前国际上比较先进的两种方法DTV和kt-SLR进行比较,实验结果表明该算法在重建精度方面具有一定优势。     

Capturing Intention‐based Full‐Frame Video Stabilization

Annoying shaky motion is one of the significant problems in home videos, since hand shake is an unavoidable effect when capturing by using a hand‐held camcorder. Video stabilization is an important technique to solve this problem, but the stabilized videos resulting from some current methods usually have decreased resolution and are still not so stable. In this paper, we propose a robust and practical method of full‐frame video stabilization while considering user's capturing intention to remove not only the high frequency shaky motions but also the low frequency unexpected movements. To guess the user's capturing intention, we first consider the regions of interest in the video to estimate which regions or objects the user wants to capture, and then use a polyline to estimate a new stable camcorder motion path while avoiding the user's interested regions or objects being cut out. Then, we fill the dynamic and static missing areas caused by frame alignment from other frames to keep the same resolution and quality as the original video. Furthermore, we smooth the discontinuous regions by using a three‐dimensional Poisson‐based method. After the above automatic operations, a full‐frame stabilized video can be achieved and the important regions and objects can also be preserved.     

Vision based smoke detection system using image energy and color information

Smoke detection is a crucial task in many video surveillance applications and could have a great impact to raise the level of safety of urban areas. Many commercial smoke detection sensors exist but most of them cannot be applied in open space or outdoor scenarios. With this aim, the paper presents a smoke detection system that uses a common CCD camera sensor to detect smoke in images and trigger alarms. First, a proper background model is proposed to reliably extract smoke regions and avoid over-segmentation and false positives in outdoor scenarios where many distractors are present, such as moving trees or light reflexes. A novel Bayesian approach is adopted to detect smoke regions in the scene analyzing image energy by means of the Wavelet Transform coefficients and Color Information. A statistical model of image energy is built, using a temporal Gaussian Mixture, to analyze the energy decay that typically occurs when smoke covers the scene then the detection is strengthen evaluating the color blending between a reference smoke color and the input frame. The proposed system is capable of detecting rapidly smoke events both in night and in day conditions with a reduced number of false alarms hence is particularly suitable for monitoring large outdoor scenarios where common sensors would fail. An extensive experimental campaign both on recorded videos and live cameras evaluates the efficacy and efficiency of the system in many real world scenarios, such as outdoor storages and forests.