Dense-CNN: Dense convolutional neural network for stereo matching using multiscale feature connection

In spite of the fact that convolutional neural network-based stereo matching models have shown good performance in both accuracy and robustness, the issue of image feature loss in regions of texture-less, complex scenes and occlusions remains. In this paper, we present a dense convolutional neural network-based stereo matching method with multiscale feature connection, named Dense-CNN. First, we construct a novel densely connected network with multiscale convolutional layers to extract rich image features, in which the merged multiscale features with context information are utilized to estimate the cost volume for stereo matching. Second, we plan a novel loss-function strategy to learn the network parameters more reasonably, which can develop the performance of the proposed Dense-CNN model on disparity computation. Finally, we run our Dense-CNN model on the Middlebury and KITTI databases to conduct a comprehensive comparison with several state-of-the-art approaches. The experimental results demonstrate that the proposed method achieved superior performance on computational accuracy and robustness of disparity estimation, especially achieving the significant benefit of feature preservation in ill-posed regions.     

A Object-oriented Landslide Extraction Method based on Morphological Opening Operation

Landslide is one of the most common geological disasters,and it is of great significance to quickly and accurately obtain the hazard degree and distribution of landslide.By introducing the morphological opening operation and the regional level set algorithm to construct the object\|oriented landslide extraction method,and using the multi\|spectral image of GF\|2 satellite as the data source,the landslide extraction experiment is carried out by using the constructed method and the landslide extraction method based on the pixel in the study area of the Bagmati area in Nepal,and the results of the landslide extraction are analyzed.The experimental results show that the object\|oriented landslide extraction method is more accurate than the extraction method based on the pixel,and the anti\|interference ability of the clouds and snows is stronger than that of the pixel\|based landslide extraction method.     

Analysis of multiscale mortar mixed approximation of nonlinear elliptic equations

A multiscale mortar mixed finite element method is established to approximate non-linear second order elliptic equations. The method is based on non-overlapping domain decomposition and mortar finite element methods. The existence and uniqueness of the approximation are demonstrated, and a priori $L^2$-error estimates for the velocity and pressure are derived. An error bound for mortar pressure is proved. Convergence estimates of the mortar pressure are based on a linear interface formulation having the discrete-pressure dependent coefficient. Optimal order convergence is achieved on the fine scale by a proper choice of mortar space and polynomial degree of approximation. The quadratic convergence of the Newton–Raphson method is proved for the nonlinear algebraic system arising from the mortar mixed formulation of the problem. Numerical experiments are performed to support theoretic results.     

No-reference screen content video quality assessment

How to effectively and accurately measure the degradation of media content is an important research topic in the field of image or video processing. Application scenarios such as online meetings, distance learning, and live game streaming make screen content video become a hot spot in Video Quality Assessment (VQA) research. However, to the best of our knowledge, there is currently no no-reference VQA model designed specifically for screen content videos. In this paper, we propose a blind VQA model for screen content videos. This model first uses a multi-scale approach to extract several groups of features, including gradient features, relative standard deviation features, compression features, frequency domain features and inter-frame features. Through training with labeled videos, the model then uses support vector regressor to map the frame feature vectors to video quality scores. We validate the model on the CSCVQ database. Experiments show that our proposed model outperforms the existing full- and no-reference quality evaluation metrics and is also competitive in terms of stability and computational efficiency.     

Control and optimization of multiscale process systems

In this work, we present an overview of recently developed methods for control and optimization of complex process systems described by multiscale models. We primarily discuss methods developed in the context of our previous research work and use examples of thin film growth processes to motivate the development of these methods and illustrate their application.     

改进的Curvelet变换图像降噪方法

与小波变换相比,Curvelet变换等多尺度几何分析方法,可以更好地逼近含线奇异的高维函数。基于Curvelet变换的图像去噪方法,即Window Shrink算法,考虑到Curvelet变换系数之间的相关性,利用软阈值方法降噪。通过窗口邻域操作,对变换后的每一个Curvelet系数自适应地进行萎缩处理,降低噪声系数权重以提高信噪比。实验表明,该方法一定程度上改进了传统Curvelet去噪方法“过扼杀”Curvelet变换系数的缺点,可以较好地保持图像边缘。在噪声方差σ=25时,小波,Curvelet以及Window Shrink去噪算法的峰值信噪比（PSNR）分别为28．59、29．25和29．93,后者明显优于前二者。     

基于小波域多状态隐马尔科夫树模型多尺度文本图像分割

该文基于小波域多状态隐马尔科夫树（HMT）模型，引入一种新的文本分割方法。该分割方法是在H.Choi et al.(2001)工作的基础上，将文本按纹理分为背景、文字与图片3种类型，分别建立多状态HMT模型。另外，基于平滑图像将上述方法又作了进一步的改进，引入了多状态IHMT分割方法，最后通过实例阐明了方法的有效性。     

基于小波多尺度积的图像增强新算法

在分析小波变换对噪声影响规律的基础上,结合小波多尺度积的特性,提出了一种基于小波多尺度积的图像增强新算法,利用二进小波变换中各尺度上小波系数间的相关性,有效改善了图像增强过程中噪声放大问题.     

Semi-blind quality estimation of compressed videos using digital watermarking

A semi-blind method that estimates the quality of a video distorted by H.264/AVC compression/decompression is introduced. The method embeds pseudo-random binary watermarks in the I-frames of the original undistorted video. To assess the quality of a segment of a distorted watermarked video, the watermark bits are extracted and the quality is estimated based on the similarity between the embedded and the extracted watermarks. To enable quality assessment for a large range of distortions, the derivative vectors of different scaled versions of each I-frame of the original video are obtained, using wavelet transform. The watermark bits that are embedded in the small wavelet scales are used to estimate the small distortions, while the bits embedded in the large wavelet scales are used to estimate large distortions. This is because the latter bits are more robust (to distortions) than those bits embedded in the small wavelet scales. The proposed method was tested on different video sequences which were distorted by compression/decompression using H.264/AVC with different quality factors. The simulation results show that the proposed method can accurately estimate the quality of a video and its frames in terms of the peak signal-to-noise ratio (PSNR) and the structural similarity (SSIM) quality measures.