摘要:In this paper,a video compressed sensing reconstruction algorithm based on multidimensional reference frames is proposed using the sparse characteristics of video signals in different sparse representation domains.First,the overall structure of the proposed video compressed sensing algorithm is introduced in this paper.The paper adopts a multi-reference frame bidirectional prediction hypothesis optimization algorithm.Then,the paper proposes a reconstruction method for CS frames at the re-decoding end.In addition to using key frames of each GOP reconstructed in the time domain as reference frames for reconstructing CS frames,half-pixel reference frames and scaled reference frames in the pixel domain are also used as CS frames.Reference frames of CS frames are used to obtain higher quality assumptions.Themethod of obtaining reference frames in the pixel domain is also discussed in detail in this paper.Finally,the reconstruction algorithm proposed in this paper is compared with video compression algorithms in the literature that have better reconstruction results.Experiments show that the algorithm has better performance than the best multi-reference frame video compression sensing algorithm and can effectively improve the quality of slowmotion video reconstruction.
摘要:Image generation is a hot topic in the academic recently,and has been applied to AI drawing,which can bring Vivid AI paintings without labor costs.In image generation,we represent the image as a random vector,assuming that the images of the natural scene obey an unknown distribution,we hope to estimate its distribution through some observation samples.Especially,with the development of GAN(Generative Adversarial Network),The generator and discriminator improve the model capability through adversarial,the quality of the generated image is also increasing.The image quality generated by the existing GAN based image generation model is so well-paint that it can be passed for genuine one.Based on the brief introduction of the concept ofGAN,this paper analyzes themain ideas of image synthesis,studies the representative SOTA GAN based Image synthesis method.
摘要:Deep learning is widely used in artificial intelligence fields such as computer vision,natural language recognition,and intelligent robots.With the development of deep learning,people’s expectations for this technology are increasing daily.Enterprises and individuals usually need a lot of computing power to support the practical work of deep learning technology.Many cloud service providers provide and deploy cloud computing environments.However,there are severe risks of privacy leakage when transferring data to cloud service providers and using data for model training,which makes users unable to use deep learning technology in cloud computing environments confidently.This paper mainly reviews the privacy leakage problems that exist when using deep learning,then introduces deep learning algorithms that support privacy protection,compares and looks forward to these algorithms,and summarizes this aspect’s development.
摘要:With the popularization of high-performance electronic imaging equipment and the wide application of digital image editing software,the threshold of digital image editing becomes lower and lower.Thismakes it easy to trick the human visual system with professionally altered images.These tampered images have brought serious threats to many fields,including personal privacy,news communication,judicial evidence collection,information security and so on.Therefore,the security and reliability of digital information has been increasingly concerned by the international community.In this paper,digital image tamper detection methods are classified according to the clues that they rely on,detection methods based on image content and detection methods based on double JPEG compression traces.This paper analyzes and discusses the important algorithms in several classification methods,and summarizes the problems existing in various methods.Finally,this paper predicts the future development trend of tamper detection.
摘要:In this paper,we study the state-dependent interference channel,where the Rayleigh channel is non-causally known at cognitive network.We propose an active secondary transmission mechanism with interference cancellation technique according to the ON-OFF status of primary network.the secondary transmission mechanism is divided into four cases according to the active state of the primary user in the two time slots.For these interference cases,numerical results are provided to show that active interference cancellation mechanism significantly reduces the secondary transmission performance in terms of secondary outage probability and energy efficiency.