We present an iterative adaptive hybrid image restoration algorithm for fast convergence. The local variance, mean, and maximum values are used to constrain the solution space. These parameters are computed at each iteration step using a partially restored image at each iteration, and they are used to impose the degree of local smoothness on the solution. The resulting iterative algorithm exhibits increased convergence speed and better performance than typical regularized constrained least-squares approach.
We present a regularized mixed norm multichannel image restoration algorithm. The problem of multichannel restoration using both within- and between-channel deterministic information is considered. For each channel a functional that combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed. We introduce a mixed norm parameter that controls the relative contribution between the LMS and the LMF, and a regularization parameter that defines the degree of smoothness of the solution, both updated at each iteration according to the noise characteristics of each channel. The novelty of the proposed algorithm is that no knowledge of the noise distribution for each channel is required, and the parameters just mentioned are adjusted based on the partially restored image.
To clearly identify the face given in a surveillance image, this paper proposes a new method that magnifies the face image large enough and brings the magnified face image in focus. For this purpose, the compound lens system consisted of the zooming and focusing lenses is analysed to derive the relationship between the positions of lenses and the image size. Once the face size in the surveillance image and the target face size to achieve are given, the positions of the lenses are determined by the derived relationship. To adjust the positions of the lenses to obtain the focused image, the four point measurement algorithm is proposed. It calculates the focus measures of at most 4 positions and estimates the position having the maximum focus measure. The algorithm has been implemented on the camera system whose lenses are controlled by fast motors. The experimental results have shown that the magnified and focused image can be obtained in 0.77 seconds on average.
This paper addresses an adaptive motion vector prediction algorithm to improve the performance of video encoder. The block-based motion vector is characterized by the local statistics so that the coefficients of LS-based linear motion predictor can be optimized. However, it requires very expensive computational cost. The proposed algorithm using LS approach with spatially varying motion-directed property adaptively controls the coefficients of the motion predictor and reduces the computational cost as well as the motion prediction error. Experimental results show the capability of the proposed algorithm.
In this paper, we present an adaptive motion search range decision algorithm for low-power video encoder. The performance, computation, and power consumption of block matching motion estimation algorithms in video coding standards depends on the motion search range. The motion search range to restrict the motion candidates controls the trade-off between the motion accuracy and the encoder complexity. The proposed algorithm adaptively determines the motion search range by local statistics of the neighbor blocks, resulting in dramatic
reduction of computational cost of video encoder without the loss of coding efficiency. Experimental results show that the proposed algorithm speeds up encoding time by 1.5-2.8 times, and reduces power consumption to 2.5-7.0 times.
In this paper, an efficient separable one-dimensional loop and post-filtering algorithm is addressed to suppress blocking and ringing artifacts of H.26L compressed video. A new one- dimensional pixel-based regularized smoothing function is defined and the regularization parameters controlling the degree of smoothness to two neighboring directions are determined by available information in encoder and decoder. The proposed loop/post filter is different to the typical regularization approaches, in that the proposed regularized smoothing functional is defined on pixel basis for easy and fast implementation. Therefore, no inverse matrix is required and iteration techniques are not necessary, which require very expensive computational cost. Also, by using look-up-table for determining the regularization parameters, the recovered image can be obtained with less computational cost. The experimental results show the capability of the proposed algorithm.
This paper introduces a regularized mixed-norm image restoration algorithm. A functional which combines the least mean squares (LMS), the least mean fourth (LMF), and a smoothing functional is proposed.A function of the kurtosis is used to determine the relative importance between the LMS and the LMF functionals, and a function of the previous two functionals an the smoothing functional is utilized for determining the regularization parameter. The two parameters are chosen in such a way that the proposed functional is convex, so that a local minimizer becomes a global minimizer. The novelty of the proposed algorithm is than no knowledge of the noise distribution is required, and the relative contribution of the LMS, the LMF and the smoothing functional is adjusted based on the partially restored image.
This paper introduces an iterative regularized approach to obtain a high resolution video sequence. A multiple input smoothing convex functional is defined and used to obtain a globally optimal high resolution video sequence. A mathematical model of multiple inputs is described by using the point spread function between the original and bilinearly interpolated images in the spatial domain, and motion estimation between frames in the temporal domain. Properties of the proposed smoothing convex functional are analyzed. An iterative algorithm is utilized for obtaining a solution. The regularization parameter is updated at each iteration step from the partially restored video sequence. Experimental results demonstrate the capability of the proposed approach.
In this paper, we propose an iterative regularized error concealment algorithm. The coded image can be degraded due to channel errors, network congestion, and switching system problems. We may have therefore seriously degraded images due to information loss. When the structure of the image and video codec is hierarchical, the degradation may be worse because of the inter-dependence of the coded information. In order to solve the error concealment problem of compressed images, we use an iterative regularized algorithm. We analyze the necessity of an oriented high pass operator we introduce and the requirement of changing the initial condition when all the quantized DCT coefficients in a block are lost. Several experimental results are presented.