A compact representation of the Green function is proposed by applying the discrete wavelet concept in the k-domain, which can be used for the acceleration of scattered field calculations in integral equation methods. A mathematical expression of the Green function based on the discrete wavelet concept is derived and its characteristics are discussed.

This paper describes an image synthesis method based on an estimation of camera parameters. In order to acquire high quality images using image synthesis, we take some constraints into account, which include angle of view, synchronization of change of scale and change of viewing direction. The proposed method is based on an investigation that any camera operation containing a change of scale and a pure 3D rotation can be represented by a 2D geometric transformation. The transformation can explain all the synthesis procedure consisting of locating, synchronizing, and operating images. The procedure is described based on a virtual camera which is constituted of a virtual viewing point and a virtual image plain. The method can be efficiently implemented in such a way that each image to be synthesized undergoes the transformation only one time. The parameters in the image transformation are estimated from image sequence. The estimation scheme consists of first establishing correspondence and then estimating the parameters by fitting the correspondence data to the transformation model. We present experimental results and show the validity of the proposed method.

This Letter proposes a new three dimensional (3D) visual communication approach based on the image-based rendering. We first compactly represent a reference view set by exploiting its geometric correlation and then efficiently compress the representation with appropriate coding schemes. Experimental results demonstrate that our proposed method significantly reduces the required bitrate.

In this paper, a general formula of disparity estimation based on Bayesian Maximum A Posteriori (MAP) algorithm is derived and implemented with simplified probabilistic models. The formula is the generalized probabilistic diffusion equation based on Bayesian model, and can be implemented into some different forms corresponding to the probabilistic models in the disparity neighborhood system or configuration. The probabilistic models are independence and similarity among the neighboring disparities in the configuration. The independence probabilistic model guarantees the discontinuity at the object boundary region, and the similarity model does the continuity or the high correlation of the disparity distribution. According to the experimental results, the proposed algorithm had good estimation performance. This result showes that the derived formula generalizes the probabilistic diffusion based on Bayesian MAP algorithm for disparity estimation. Also, the proposed probabilistic models are reasonable and approximate the pure joint probability distribution very well with decreasing the computations to O(n()) from O(n()4) of the generalized formula.

Side match vector quantization (SMVQ) has been originally developed for image compression and is also useful for steganography. SMVQ requires to create its own state codebook for each block in both encoding and decoding phases. Since the conventional method for the state codebook generation is extremely time-consuming, this letter proposes a fast generation method. The proposed method is tens times faster than the conventional one without loss of perceptual visual quality.

Image deformations caused by different steganographic methods are typically extremely small and highly similar, which makes their detection and identification to be a difficult task. Although recent steganalytic methods using deep learning have achieved high accuracy, they have been made to detect stego images to which specific steganographic methods have been applied. In this letter, a staganalytic method is proposed that uses hierarchical residual neural networks (ResNet), allowing detection (i.e. classification between stego and cover images) and identification of four spatial steganographic methods (i.e. LSB, PVD, WOW and S-UNIWARD). Experimental results show that using hierarchical ResNets achieves a classification rate of 79.71% in quinary classification, which is approximately 23% higher compared to using a plain convolutional neural network (CNN).

To make the best use of the known characteristics of the alternating-direction-implicit finite-difference time-domain (ADI-FDTD) method such as unconditional stability and modeling accuracy, an efficient time domain solution with variable time-step size is proposed. Numerical results show that a time-step size for a given mesh size can be increased preserving a desired numerical accuracy over frequencies of interest.