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The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer-Convolution fusion dehazing network (TCFDN), which uses Transformer's global modeling ability and convolutional neural network's local modeling ability to improve the dehazing ability. In the Transformer-Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer-Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer-Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends.

The rapid growth of digital multimedia and Internet technologies has made copyright protection, copy protection, and integrity verification three important issues in the digital world. To solve these problems, the digital watermarking technique has been presented and widely researched. Traditional watermarking algorithms are mostly based on discrete transform domains, such as the discrete cosine transform, discrete Fourier transform (DFT), and discrete wavelet transform (DWT). Most of these algorithms are good for only one purpose. Recently, some multipurpose digital watermarking methods have been presented, which can achieve the goal of content authentication and copyright protection simultaneously. However, they are based on DWT or DFT. Lately, several robust watermarking schemes based on vector quantization (VQ) have been presented, but they can only be used for copyright protection. In this paper, we present a novel multipurpose digital image watermarking method based on the multistage vector quantizer structure, which can be applied to image authentication and copyright protection. In the proposed method, the semi-fragile watermark and the robust watermark are embedded in different VQ stages using different techniques, and both of them can be extracted without the original image. Simulation results demonstrate the effectiveness of our algorithm in terms of robustness and fragility.

With the rise of short video and the development of self-media, video has become a type of frequently used data, thus the protection of video data is particularly important. Digital watermarking is a very effective digital authentication and copyright protection technology that hides a piece of data called watermark into digital multimedia content. However, most existing video watermark algorithms fail to deal with the videos under geometric distortions, and they only focus on copyright protection, ignoring the task of tamper detection. This paper proposes a multipurpose video watermarking algorithm that is resistant to rotation and scaling attacks while providing both copyright protection and tamper detection. We design a DFT template embedding method to make the watermarking algorithm resistant to rotation and scaling attacks efficiently. This method can detect the rotation and scaling that the video has undergone during the watermark extraction process, and then extract the copyright watermark information based on geometric correction. In addition, we propose a robust watermark embedding method with tamper detection function, which embeds copyright watermark information with validation bits in the DCT coefficients of I frames. By embedding the copyright watermark block by block, the algorithm can not only improve the robustness, but also enable the system to detect tampering. Experiments show that the proposed algorithm has good robustness to various attacks such as compression, filtering, rotation, scaling, frame rate changing and other inter-frame operations, and it outperforms previous geometrically robust algorithm by an average of 12.8% under geometric attacks on the normalized correlation values. Also, we show that our algorithm is effective in tamper detection by localizing the distorted areas in the video frames. [ABSTRACT FROM AUTHOR]

The purpose of image dehazing is to remove the interference from weather factors in degraded images and enhance the clarity and color saturation of images to maximize the restoration of useful features. Single image dehazing is one of the most important tasks in the field of image restoration. In recent years, due to the progress of deep learning, single image dehazing has made great progress. With the success of Transformer in advanced computer vision tasks, some research studies also began to apply Transformer to image dehazing tasks and obtained surprising results. However, both the deconvolution-neural-network-based dehazing algorithm and Transformer based dehazing algorithm magnify their advantages and disadvantages separately. Therefore, this paper proposes a novel Transformer–Convolution fusion dehazing network (TCFDN), which uses Transformer's global modeling ability and convolutional neural network's local modeling ability to improve the dehazing ability. In the Transformer–Convolution fusion dehazing network, the classic self-encoder structure is used. This paper proposes a Transformer–Convolution hybrid layer, which uses an adaptive fusion strategy to make full use of the Swin-Transformer and convolutional neural network to extract and reconstruct image features. On the basis of previous research, this layer further improves the ability of the network to remove haze. A series of contrast experiments and ablation experiments not only proved that the Transformer–Convolution fusion dehazing network proposed in this paper exceeded the more advanced dehazing algorithm, but also provided solid and powerful evidence for the basic theory on which it depends. [ABSTRACT FROM AUTHOR]

The development of the Internet, together with the progress of multimedia processing techniques, has led to the problems of data piracy, data tampering and illegal dissemination. Digital watermarking is an effective approach to data authentication and copyright protection. This paper proposes a geometrically robust multi-bit video watermarking algorithm based on 2-D DFT (two-dimensional discrete Fourier transform). While most of the existing video watermarking schemes require synchronization to extract the watermark from rotated or scaled videos, which is time-consuming and affects the accuracy, the proposed method can do direct extraction without performing synchronization for videos attacked by rotation, scaling or cropping. For embedding the watermark, circular templates in DFT domain are transformed into spatial masks and added to the video frames in spatial domain. A perceptual model based on local contrast is applied to keep the fidelity of the watermarked video. We also propose an accurate and efficient extraction method which is based on the cross-correlation between the Wiener-filtered DFT magnitude and the stretched template sequence in polar coordinates. Experimental results show that the proposed algorithm is robust against various kinds of attacks, such as compression, filtering, rotation, scaling, cropping, frame averaging and frame rate changing. [ABSTRACT FROM AUTHOR]

Vector quantization (VQ) has been successfully used in data compression and feature extraction areas. Codebook design is the essential step of VQ. The K-means algorithm is a famous data clustering technique which is also an efficient codebook design scheme. The main disadvantages of K-means algorithm lie in that the initial cluster centroids greatly affect the convergence speed and the final clustering performance. In the past two decades, many codebook initialization techniques have been proposed. However, most of these techniques do not make full use of the features of the training vectors, and some techniques require high extra computational load. This paper presents an efficient and simple technique for the conventional K-means algorithm based on feature classification and grouping. Firstly, all training vectors are classified into sixteen categories based on a two-level classifier including an edge classifier and a contrast classifier. Then, the training vectors in each category are sorted based on their norm values and divided into groups. Each group has the same size, and the centroid vector of each group is calculated as an initial codeword. Experimental results show that, compared with several typical initialization techniques, our technique can obtain a better codebook along with a faster convergence speed in a shorter time. [ABSTRACT FROM AUTHOR]

With the popularization of media-capture devices and the development of the Internet's basic facilities, video has become the most popular media information in recent years. The massive capacity of video imposes the demand of automatic video identification techniques which are very important to various applications such as content based video retrieval and copy detection. Therefore, as a challenging problem, video identification has drawn more and more attention in the past decade. The problem addressed here is to identify a given video clip in a given set of video sequences. In this paper, a robust video identification algorithm based on local non-negative matrix factorization (LNMF) is presented. First, some concepts about LNMF are described and the way of finding the factorized matrix is given. Then, its convergence is proven. In addition, a LNMF based shot detection method is proposed for constructing a video identification framework completely based on LNMF. Finally, a LNMF based identification approach using Hausdorff distance is introduced and a two-stage search process is proposed. Experimental results show the robustness of the proposed approach to many kinds of content-preserved distortions and its superiority to other algorithms. [ABSTRACT FROM AUTHOR]

Abstract: Kernel discriminant analysis (KDA) is effective to extract nonlinear discriminative features of input samples using the kernel trick. However, the conventional KDA algorithm endures the kernel selection which has significant impact on the performances of KDA. In order to overcome this limitation, a novel nonlinear feature extraction method called adaptive quasiconformal kernel discriminant analysis (AQKDA) is proposed in this paper. AQKDA maps the data from the original input space to the high dimensional kernel space using a quasiconformal kernel. The adaptive parameters of the quasiconformal kernel are automatically calculated through optimizing an objective function designed for measuring the class separability of data in the feature space. Consequently, the nonlinear features extracted by AQKDA have the larger class separability compared with KDA. Experimental results on the two real-world datasets demonstrate the effectiveness of the proposed method. [Copyright &y& Elsevier]