Generalized-KFCS: Motion estimation enhanced Kalman filtered compressive sensing for video

In this paper, we propose a Generalized Kalman Filtered Compressive Sensing (Generalized-KFCS) framework to reconstruct a video sequence, which relaxes the assumption of a slowly changing sparsity pattern in Kalman Filtered Compressive Sensing [1, 2, 3, 4]. In the proposed framework, we employ motion estimation to achieve the estimation of the state transition matrix for the Kalman filter, and then reconstruct the video sequence via the Kalman filter in conjunction with compressive sensing. In addition, we propose a novel method to directly apply motion estimation to compressively sensed samples without reconstructing the video sequence. Simulation results demonstrate the superiority of our algorithm for practical video reconstruction.This work was partially supported by EPSRC Research Grant EP/K033700/1, the Fundamental Research Funds for the Central Universities (No. 2014JBM149), and the Scientific Research Foundation for the Returned Overseas Chinese Scholars (of State Education Ministry).This is the author accepted manuscript. The final version is available from IEEE via http://dx.doi.org/10.1109/ICIP.2014.702525

Frame-Recurrent Video Super-Resolution

Recent advances in video super-resolution have shown that convolutional neural networks combined with motion compensation are able to merge information from multiple low-resolution (LR) frames to generate high-quality images. Current state-of-the-art methods process a batch of LR frames to generate a single high-resolution (HR) frame and run this scheme in a sliding window fashion over the entire video, effectively treating the problem as a large number of separate multi-frame super-resolution tasks. This approach has two main weaknesses: 1) Each input frame is processed and warped multiple times, increasing the computational cost, and 2) each output frame is estimated independently conditioned on the input frames, limiting the system's ability to produce temporally consistent results. In this work, we propose an end-to-end trainable frame-recurrent video super-resolution framework that uses the previously inferred HR estimate to super-resolve the subsequent frame. This naturally encourages temporally consistent results and reduces the computational cost by warping only one image in each step. Furthermore, due to its recurrent nature, the proposed method has the ability to assimilate a large number of previous frames without increased computational demands. Extensive evaluations and comparisons with previous methods validate the strengths of our approach and demonstrate that the proposed framework is able to significantly outperform the current state of the art.Comment: Accepted at CVPR 201

An Efficient Algorithm for Video Super-Resolution Based On a Sequential Model

In this work, we propose a novel procedure for video super-resolution, that is the recovery of a sequence of high-resolution images from its low-resolution counterpart. Our approach is based on a "sequential" model (i.e., each high-resolution frame is supposed to be a displaced version of the preceding one) and considers the use of sparsity-enforcing priors. Both the recovery of the high-resolution images and the motion fields relating them is tackled. This leads to a large-dimensional, non-convex and non-smooth problem. We propose an algorithmic framework to address the latter. Our approach relies on fast gradient evaluation methods and modern optimization techniques for non-differentiable/non-convex problems. Unlike some other previous works, we show that there exists a provably-convergent method with a complexity linear in the problem dimensions. We assess the proposed optimization method on {several video benchmarks and emphasize its good performance with respect to the state of the art.}Comment: 37 pages, SIAM Journal on Imaging Sciences, 201

An augmented Lagrangian method for total variation video restoration,”

Abstract-This paper presents a fast algorithm for restoring video sequences. The proposed algorithm, as opposed to existing methods, does not consider video restoration as a sequence of image restoration problems. Rather, it treats a video sequence as a space-time volume and poses a space-time total variation regularization to enhance the smoothness of the solution. The optimization problem is solved by transforming the original unconstrained minimization problem to an equivalent constrained minimization problem. An augmented Lagrangian method is used to handle the constraints, and an alternating direction method (ADM) is used to iteratively find solutions of the subproblems. The proposed algorithm has a wide range of applications, including video deblurring and denoising, video disparity refinement, and hot-air turbulence effect reduction

Video-to-Video Dynamic Super-Resolution for Grayscale and Color Sequences

We address the dynamic super-resolution (SR) problem of reconstructing a high-quality set of monochromatic or color superresolved images from low-quality monochromatic, color, or mosaiced frames. Our approach includes a joint method for simultaneous SR, deblurring, and demosaicing, this way taking into account practical color measurements encountered in video sequences. For the case of translational motion and common space-invariant blur, the proposed method is based on a very fast and memory efficient approximation of the Kalman filter (KF). Experimental results on both simulated and real data are supplied, demonstrating the presented algorithms, and their strength

Picture processing for enhancement and recognition

Recent years have been characterized by an incredible growth in computing power and storage capabilities, communication speed and bandwidth availability, either for desktop platform or mobile device. The combination of these factors have led to a new era of multimedia applications: browsing of huge image archives, consultation of online video databases, location based services and many other. Multimedia is almost everywhere and requires high quality data, easy retrieval of multimedia contents, increase in network access capacity and bandwidth per user. To meet all the mentioned requirements many efforts have to be made in various research areas, ranging from signal processing, image and video analysis, communication protocols, etc. The research activity developed during these three years concerns the field of multimedia signal processing, with particular attention to image and video analysis and processing. Two main topics have been faced: the first is relating to image and video reconstruction/restoration (using super resolution techniques) in web based application for multimedia contents' fruition; the second is relating to image analysis for location based systems in indoor scenario. The first topic is relating to image and video processing, in particular the focus has been put on the development of algorithm for super resolution reconstruction of image and video sequences in order to make easier the fruition of multimedia data over the web. On one hand, latest years have been characterized by an incredible proliferation and surprising success of user generated multimedia contents, and also distributed and collaborative multimedia database over the web. This brought to serious issues related to their management and maintenance: bandwidth limitation and service costs are important factors when dealing with mobile multimedia contents’ fruition. On the other hand, the current multimedia consumer market has been characterized by the advent of cheap but rather high-quality high definition displays. However, this trend is only partially supported by the deployment of high-resolution multimedia services, thus the resulting disparity between content and display formats have to be addressed and older productions need to be either re-mastered or postprocessed in order to be broadcasted for HD exploitation. In the presented scenario, superresolution reconstruction represents a major solution. Image or video super resolution techniques allow restoring the original spatial resolution from low-resolution compressed data. In this way, both content and service providers, not to tell the final users, are relieved from the burden of providing and supporting large multimedia data transfer. The second topic addressed during my Phd research activity is related to the implementation of an image based positioning system for an indoor navigator. As modern mobile device become faster, classical signal processing is suggested to be used for new applications, such location based service. The exponential growth of wearable devices, such as smartphone and PDA in general, equipped with embedded motion (accelerometers) and rotation (gyroscopes) sensors, Internet connection and high-resolution cameras makes it ideal for INS (Inertial Navigation System) applications aiming to support the localization/navigation of objects and/or users in an indoor environment where common localization systems, such as GPS (Global Positioning System), fail. Thus the need to use alternative positioning techniques. A series of intensive tests have been carried out, showing how modern signal processing techniques can be successfully applied in different scenarios, from image and video enhancement up to image recognition for localization purpose, providing low costs solutions and ensuring real-time performance