In this paper, we propose a method for video reflection removal using a video restoration framework with enhanced deformable networks (EDVR). We examine the effect of each module in EDVR on video reflection removal and modify the models using 3D convolutions. The performance of each modified model is evaluated in terms of the RMSE between the structural similarity (SSIM) and the smoothed SSIM representing temporal consistency.

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.

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.

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.

An automatic and efficient algorithm for removal of intensity flicker is proposed. The novel repair process is founded on the block-based estimation and restoration algorithm with regard to luminance variation. It is easily realized and controlled to remove most intensity flicker and preserve the wanted effects, like fade in and fade out.

In this paper we present a 3D adaptive nonlinear filter, namely the 3D adaptive CPWLN, based on the Canonical Piece Wise-Linear Network with an LMS L-filter type of adaptation. This filter is used to equalize nonlinear channel effect and remove impulsive/or mixed impulsive and Additive White Gaussian noise from video sequences. First, motion compensation is performed by a robust estimator. Then, a 3-D CPWLN LMS L-filter is applied. The overall combination is able to adequately remove undesired effects of communication channel and noise. Computer simulations on real-world image sequences are included. The algorithm yields promising results in terms of both objective and subjective quality of the restored sequence.

An important area in visual communications is the restoration of image sequences degraded by channel and noise. Since a nonlinearity is commonly involved in image transmitting procedure, an adaptive nonlinear equalizer is required. In this paper we address this problem by proposing a 3D adaptive nonlinear filter, namely the 3D adaptive Volterra filter with an LMS type of adaptation algorithm. This adaptive filter is used for equalizing an unknown 2-D channel with some point-wise nonlinearity and restoring image sequences degraded by this channel. Prior to filtering, motion is estimated from the sequence and compensated for. For this purpose, a robust region-recursive Higher Order Statistics (HOS) based motion estimation method is employed. The overall combination is able to adequately remove undesired effects of communication channel and noise. The performance of this algorithm is examined using real image sequences demonstrated by experimental results.