In this paper, a method for adaptive reconstruction of missing textures based on kernel canonical correlation analysis (CCA) with a new clustering scheme is presented. The proposed method estimates the correlation between two areas, which respectively correspond to a missing area and its neighboring area, from known parts within the target image and realizes reconstruction of the missing texture. In order to obtain this correlation, the kernel CCA is applied to each cluster containing the same kind of textures, and the optimal result is selected for the target missing area. Specifically, a new approach monitoring errors caused in the above kernel CCA-based reconstruction process enables selection of the optimal result. This approach provides a solution to the problem in traditional methods of not being able to perform adaptive reconstruction of the target textures due to missing intensities. Consequently, all of the missing textures are successfully estimated by the optimal cluster's correlation, which provides accurate reconstruction of the same kinds of textures. In addition, the proposed method can obtain the correlation more accurately than our previous works, and more successful reconstruction performance can be expected. Experimental results show impressive improvement of the proposed reconstruction technique over previously reported reconstruction techniques.

A blur restoration scheme for images with linear motion blurred objects in still background is proposed. The proposed scheme starts from a rough detection of locations of blurred objects. This rough segmentation of the blurred regions is based on an analysis of local orientation map. Then, parameters of blur are identified based on a linear constant-velocity motion blur model for every detected blurred region. After the blur parameters are estimated, the locations of blurred objects can be refined before going to a restoration process by using information from the identified blur parameters. Blur locations are refined by observing local power of the blurred image which is filtered by a high-pass filter. The high-pass filter has approximately a frequency characteristic that is complementary to the identified blur point spread function. As a final step, the image is restored by using the estimated blur parameters and locations based on an iterative deconvolution scheme applied with a regularization concept. Experimental examples of simulated and real world blurred images are demonstrated to confirm the performance of the proposed scheme.

With quick topology changes due to mobile node movement or signal fading in MANET environments, conventional routing restoration processes are costly to implement and may incur high flooding of network traffic overhead and long routing path latency. Adopting the traditional shortest path tree (SPT) recomputation and restoration schemes used in Internet routing protocols will not work effectively for MANET. An object of the next generation SPT restoration system is to provide a cost-effective solution using an adaptive learning control system, wherein the SPT restoration engine is able to skip over the heavy SPT computation. We proposed a novel SPT restoration scheme, called Cognitive Shortest Path Tree Restoration (CSPTR). CSPTR is designed based on the Network Simplex Method (NSM) and Sensitivity Analysis (SA) techniques to provide a comprehensive and low-cost link failure healing process. NSM is used to derive the shortest paths for each node, while the use of SA can greatly reduce the effort of unnecessary recomputation of the SPT when network topology changes. In practice, a SA range table is used to enable the learning capability of CSPTR. The performance of computing and communication overheads are compared with other two well-known schemes, such as Dijstra's algorithm and incremental OSPF. Results show that CSPTR can greatly eliminate unnecessary SPT recomputation and reduce large amounts of the flooding overheads.

Image restoration based on Bayesian estimation in most previous studies has assumed that the noise accumulated in an image was independent for each pixel. However, when we take optical effects into account, it is reasonable to expect spatial correlation in the superimposed noise. In this paper, we discuss the restoration of images distorted by noise which is spatially correlated with translational symmetry in the realm of probabilistic processing. First, we assume that the original image can be produced by a Gaussian model based on only a nearest-neighbor effect and that the noise superimposed at each pixel is produced by a Gaussian model having spatial correlation characterized by translational symmetry. With this model, we can use Fourier transformation to calculate system characteristics such as the restoration error and also minimize the restoration error when the hyperparameters of the probabilistic model used in the restoration process coincides with those used in the formation process. We also discuss the characteristics of image restoration distorted by spatially correlated noise using a natural image. In addition, we estimate the hyperparameters using the maximum marginal likelihood and restore an image distorted by spatially correlated noise to evaluate this method of image restoration.

A novel error concealment method using a Kalman filter is presented in this paper. In order to successfully utilize the Kalman filter, its state transition and observation models that are suitable for the video error concealment are newly defined as follows. The state transition model represents the video decoding process by a motion-compensated prediction. Furthermore, the new observation model that represents an image blurring process is defined, and calculation of the Kalman gain becomes possible. The problem of the traditional methods is solved by using the Kalman filter in the proposed method, and accurate reconstruction of corrupted video frames is achieved. Consequently, an effective error concealment method using the Kalman filter is realized. Experimental results showed that the proposed method has better performance than that of traditional methods.

This paper shows that there is a fruitful world behind sampling theorems. For this purpose, the sampling problem is reformulated from a functional analytic standpoint, and is consequently revealed that the sampling problem is a kind of inverse problem. The sampling problem covers, for example, signal and image restoration including super resolution, image reconstruction from projections such as CT scanners in hospitals, and supervised learning such as learning in artificial neural networks. An optimal reconstruction operator is also given, providing the best approximation to an individual original signal without our knowing the original signal.

In this paper, a Kalman filter-based method for restoration of video images acquired by an in-vehicle camera in foggy conditions is proposed. In order to realize Kalman filter-based restoration, the proposed method clips local blocks from the target frame by using a sliding window and regards the intensities in each block as elements of the state variable of the Kalman filter. Furthermore, the proposed method designs the following two models for restoration of foggy images. The first one is an observation model, which represents a fog deterioration model. The proposed method automatically determines all parameters of the fog deterioration model from only the foggy images to design the observation model. The second one is a non-linear state transition model, which represents the target frame in the original video image from its previous frame based on motion vectors. By utilizing the observation and state transition models, the correlation between successive frames can be effectively utilized for restoration, and accurate restoration of images obtained in foggy conditions can be achieved. Experimental results show that the proposed method has better performance than that of the traditional method based on the fog deterioration model.

In low bit-rate video transmission, the payload of a single packet can often contain a whole coded frame due to the high compression ratio in both spatial and temporal domains. Thus, the loss of a single packet can lead to the loss of a whole video frame. In this paper, we propose a novel error concealment algorithm that can effectively reconstruct the lost frame and protect the quality of video streams from the degradation caused by propagation errors. The proposed algorithm employs a bilateral motion estimation scheme where the weighted sum of the received motion vectors (MVs) in the neighboring frames is utilized to construct the MV field for the concealed frame. Unlike the conventional algorithms, the proposed scheme does not produce any overlapped pixel and hole region in the reconstructed frame. The proposed algorithm can be applied not only to the case of single frame loss but also adaptively extended to the case of multiframe loss. Experimental results show that the proposed algorithm outperforms other conventional techniques in terms of both peak signal-to-noise ratio (PSNR) performance and subjective visual quality.

Bolstering survivable backbone networks against multiple failures is becoming a common concern among telecom companies that need to continue services even when disasters occur. This paper presents a multiple-failure recovery scheme that considers the operation and management of optical paths. The presented scheme employs scheme escalation from pre-planned restoration to full rerouting. First, the survivability of this scheme against multiple failures is evaluated considering operational constraints such as route selection, resource allocation, and the recovery order of failed paths. The evaluation results show that scheme escalation provides a high level of survivability even under operational constraints, and this paper quantitatively clarifies the impact of these various operational constraints. In addition, the fundamental functions of the scheme escalation are implemented in the Generalized Multi-Protocol Label Switching control plane and verified in an optical-cross-connect-based network.

Considering the inaccuracy of image registration, we propose a new regularization restoration algorithm to solve the ill-posed super-resolution (SR) problem. Registration error is used to obtain cross-channel error information caused by inaccurate image registration. The registration error is considered as the noise mean added into the within-channel observation noise which is known as Additive White Gaussian Noise (AWGN). Based on this consideration, two constraints are regulated pixel by pixel within the framework of Miller's regularization. Regularization parameters connect the two constraints to construct a cost function. The regularization parameters are estimated adaptively in each pixel in terms of the registration error and in each observation channel in terms of the AWGN. In the iterative implementation of the proposed algorithm, sub-sampling operation and sampling aliasing in the detector model are dealt with respectively to make the restored HR image approach the original one further. The transpose of the sub-sampling operation is implemented by nearest interpolation. Simulations show that the proposed regularization algorithm can restore HR images with much sharper edges and greater SNR improvement.

A blind image restoration for non-linear motion blurs with non-uniform point spread functions based on multiple blurred versions of a same scene is proposed. The restoration is separately considered as identification and deconvolution problems. In the proposed identification process, an identification difficulty is introduced to rank an order of blur identification. A blurred image with the lowest identification difficulty is initially identified by using a single-image-based scheme. Then, other images are identified based on a cross convolution relation between each pair of blurred images. In addition, an iterative feedback scheme is applied to improve the identification results. For the deconvolution process, a spatial adaptive scheme using regional optimal terminating points is modified from a conventional iterative deconvolution scheme. The images are decomposed into sub-regions based on smoothness. The regional optimal terminating points are independently assigned to suppress a noise in smooth regions and sharpen the image in edgy regions. The optimal terminating point for each region is decided by considering a discrepancy error. Restoration examples of simulated and real world blurred images are experimented to demonstrate the performance of the proposed method.

A motion blur identification scheme is proposed for non-linear uniform motion blurs approximated by piecewise linear models which consist of more than one linear motion component. The proposed scheme includes three modules that are a motion direction estimator, a motion length estimator and a motion combination selector. In order to identify the motion directions, the proposed scheme is based on a trial restoration by using directional forward ramp motion blurs along different directions and an analysis of directional information via frequency domain by using a Radon transform. Autocorrelation functions of image derivatives along several directions are employed for estimation of the motion lengths. A proper motion combination is identified by analyzing local autocorrelation functions of non-flat component of trial restored results. Experimental examples of simulated and real world blurred images are given to demonstrate a promising performance of the proposed scheme.

Error concealment at a decoder is an efficient method to reduce the degradation of visual quality caused by channel errors. In this paper, we propose a novel spatio-temporal error concealment algorithm based on the spatial-temporal fading (STF) scheme which has been recently introduced. Although STF achieves good performance for the error concealment, several drawbacks including blurring still remain in the concealed blocks. To alleviate these drawbacks, in the proposed method, hybrid approaches with adaptive weights are proposed. First, the boundary matching algorithm and the decoder motion vector estimation which are well-known temporal error concealment methods are adaptively combined to compensate for the defect of each other. Then, an edge preserved method is utilized to reduce the blurring effects caused by the bilinear interpolation for spatial error concealment. Finally, two concealed results obtained by the hybrid spatial and temporal error concealment are pixel-wisely blended with adaptive weights. Experimental results exhibit that the proposed method outperforms conventional methods including STF in terms of the PSNR performance as well as subjective visual quality, and the computational complexity of the proposed method is similar to that of STF.

Though machine vision systems for automatically detecting visual defects, called mura, have been developed for thin flat transistor liquid crystal display (TFT-LCD) panels, they have not yet reached a level of reliability which can replace human inspectors. To establish an objective criterion for identifying real defects, some index functions for quantifying defect levels based on human perception have been recently researched. However, while these functions have been verified in the laboratory, further consideration is needed in order to apply them to real systems in the field. To begin with, we should correct the distortion occurring through the capturing of panels. Distortion can cause the defect level in the observed image to differ from that in the panel. There are several known methods to restore the observed image in general vision systems. However, TFT-LCD panel images have a unique background degradation composed of background non-uniformity and vignetting effect which cannot easily be restored through traditional methods. Therefore, in this paper we present a new method to correct background degradation of TFT-LCD panel images using principal component analysis (PCA). Experimental results show that our method properly restores the given observed images and the transformed shape of muras closely approaches the original undistorted shape.

We explain how network failures were caused by a natural disaster, describe the restoration steps that were taken, and present lessons learned from the recovery. At 21:26 on December 26th (UTC+9), 2006, there was a serious undersea earthquake off the coast of Taiwan, which measured 7.1 on the Richter scale. This earthquake caused significant damage to submarine cable systems. The resulting fiber cable failures shut down communications in several countries in the Asia Pacific networks. In the first post-earthquake recovery step, BGP routers detoured traffic along redundant backup paths, which provided poor quality connection. Subsequently, operators engineered traffic to improve the quality of recovered communication. To avoid filling narrow-bandwidth links with detoured traffic, the operators had to change the BGP routing policy. Despite the routing-level first aid, a few institutions could not be directly connected to the R&E network community because they had only a single link to the network. For these single-link networks, the commodity link was temporarily used for connectivity. Then, cable connection configurations at the switches were changed to provide high bandwidth and next-generation Internet service. From the whole restoration procedure, we learned that redundant BGP routing information is useful for recovering connectivity but not for providing available bandwidth for the re-routed traffic load and that collaboration between operators is valuable in solving traffic engineering issues such as poor-quality re-routing and lost connections of single-link networks.

We consider the blind recovery problem such that images embedded with side information are given, and we want to obtain the side information under some prescribed constraints. In this case, the system equation becomes y=Ax+b where in addition to the unknown A and x, b also is an unknown quantity and but clearly not a noise component. We assume that several images with the same embedding side information are given, and the image processing to b is described as the perturbation of A. We formulate the optimization function to obtain A, b and x, under the constraint of some finite brightness levels i.e. finite alphabets.

The objective of this paper is to survey the Generalized Multi-Protocol Label Switching (GMPLS) based recovery technology for optical transport networks. This paper introduces standardization activities of the GMPLS based recovery technology in the Internet Engineering Task Force (IETF), and recent progress of related experiments. In addition, this paper extracts requirements for the GMPLS based recovery technology through the evaluation of existing network elements, which can be client nodes of the optical transport networks. The results of field evaluations on the GMPLS based recovery technology are also introduced in this paper. Then, this paper addresses the issues for future deployment of the GMPLS based recovery technology for the optical transport networks.

The estimation of the point-spread function (PSF) is one of very important and indispensable tasks for the practical image restoration. Especially, for the motion blur, various PSF estimation algorithms have been developed so far. However, a majority of them becomes useless in the low blurred signal-to-noise ratio (BSNR) environment. This paper describes a new robust PSF estimation algorithm based on Hough transform concerning gradient vectors, which can accurately and robustly estimate the motion blur PSF even in low BSNR case. The effectiveness and validity of the proposed algorithm are verified by applying it to the PSF estimation and the image restoration for noisy and motion blurred images.

Virtual Private Networks (VPNs) are overlay networks established on top of a public network backbone with the goal of providing a service comparable to Private Networks (PNs). The recently proposed VPN hose-model provides customers with flexible and convenient ways to specify their bandwidth requirements. To meet the specified bandwidth requirements, the Network Service Provider (NSP) must reserve sufficient bandwidth on the data transmission paths between each pair of endpoints in a VPN. In addition, the reliability of a VPN depends on the reliability of the data transmission paths. Italiano et al. proposed an algorithm that finds a set of backup paths for a given VPN (VPN tree) under the single-link failure model [1]. When a link failure is detected on a VPN tree, a backup path corresponding to the failed link can be activated to restore the disconnected VPN tree into a new one, thereby ensuring the reliability of the VPN. However, Italiano's algorithm cannot guarantee that the specified bandwidth requirement of the given VPN under the single-link failure model will be met. To address this issue, we propose a new backup path set selection algorithm called BANGUAD in this paper. In addition, the problem of establishing multiple bandwidth-guaranteed hose-model VPNs under the single-link failure model has not been investigated previously. However in this problem, bandwidth-sharing algorithms have the potential to improve the performance of a provisioning algorithm significantly. Therefore, we also propose a bandwidth sharing algorithm and three provisioning algorithms for establishing multiple bandwidth-guaranteed hose-model VPNs under the single-link failure model. Simulations that compare the performance of the proposed algorithms are reported.

In this paper, we propose a new method that can remove view-disturbing noises from stereo images. One of the thorny problems in outdoor surveillance by a camera is that adherent noises such as waterdrops on the protecting glass surface lens disturb the view from the camera. Therefore, we propose a method for removing adherent noises from stereo images taken with a stereo camera system. Our method is based on the stereo measurement and utilizes disparities between stereo image pair. Positions of noises in images can be detected by comparing disparities measured from stereo images with the distance between the stereo camera system and the glass surface. True disparities of image regions hidden by noises can be estimated from the property that disparities are generally similar with those around noises. Finally, we can remove noises from images by replacing the above regions with textures of corresponding image regions obtained by the disparity referring. Experimental results show the effectiveness of the proposed method.