The types and structures of inorganic pores are key factors in evaluations of the reservoir space and distribution characteristics of shale oil and gas.However,quantitative identification methods for pores of differen...The types and structures of inorganic pores are key factors in evaluations of the reservoir space and distribution characteristics of shale oil and gas.However,quantitative identification methods for pores of different inorganic components have not yet been fully developed.For this reason,a quantitative characterization method of inorganic pores using pixel information was proposed in this study.A machine learning algorithm was used to assist the field emission scanning electron microscopy(FE-SEM)image processing of shale to realize the accurate identification and quantitative characterization of inorganic pores on the surface of high-precision images of shale with a small view.Moreover,large-view image splicing technology,combined with quantitative evaluation of minerals by scanning electron microscopy(QEMSCAN)image joint characterization technology,was used to accurately analyze the distribution characteristics of inorganic pores under different mineral components.The quantitative methods of pore characteristics of different inorganic components under the pixel information of shale were studied.The results showed that(1)the Waikato Environment for Knowledge Analysis(WEKA)machine learning model can effectively identify and extract shale mineral components and inorganic pore distribution,and the large-view FE-SEM images are representative of samples at the 200μm×200μm view scale,meeting statistical requirements and eliminating the influence of heterogeneity;(2)the pores developed by different mineral components of shale had obvious differences,indicating that the development of inorganic pores is highly correlated with the properties of shale minerals themselves;and(3)the pore-forming ability of different mineral components is calculated by the quantitative method of single component pore-forming coefficient.Chlorite showed the highest pore-forming ability,followed by(in descending order)illite,pyrite,calcite,dolomite,albite,orthoclase,quartz,and apatite.This study contributes to advancing our understanding of inorganic pore characteristics in shale.展开更多
The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steg...The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.展开更多
In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of...In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of the video content.The proposed method dynamically changed the reliability weight of the neighboring macroblock,which was used to conceal the lost macroblocks according to the packet loss rate of the current channel state.The perceptual weight map was utilized as side information to do weighted pixel interpolation and side-match based motion compensation for spatial and temporal error concealment,respectively.And the perceptual weight of the neighboring macroblocks was adaptively modified according to the perceptual weight of the lost macroblocks.Compared with the method used in H.264 joint model,experiment results show that the proposed method performs well both in subjective video quality and objective video quality,and increases the average peak signal-to-noise ratio(PSNR) of the whole frame by about 0.4 dB when the video bitstreams are transmitted with packets loss.展开更多
Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is...Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is the estimation of transmittance,which is the key element of haze-affected imaging models.Conventional methods are based on a set of assumptions that reduce the solution search space.However,the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image.In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method.The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions.This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks(CNNs)with a Parallax Attention Mechanism(PAM).It uses the differential pixel-based variance in order to estimate transmittance.The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map.The transmission map is also improved based on improved Markov random field(MRF)energy functions.We used different images to test the proposed algorithm.The entropy value of the proposed method was 7.43 and 7.39,a percent increase of4.35%and5.42%,respectively,compared to the best existing results.The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics.The proposed approach’s drawback,an over-reliance on real ground truth images,is also investigated.The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.展开更多
Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) ma...Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.展开更多
基金supported by the National Natural Science Foundation of China(42372144)the Natural Science Foundation of Xinjiang Uygur Autonomous Region(2024D01E09)the Strategic Cooperation Technology Projects of CNPC and CUPB(ZLZX2020-01-05).
文摘The types and structures of inorganic pores are key factors in evaluations of the reservoir space and distribution characteristics of shale oil and gas.However,quantitative identification methods for pores of different inorganic components have not yet been fully developed.For this reason,a quantitative characterization method of inorganic pores using pixel information was proposed in this study.A machine learning algorithm was used to assist the field emission scanning electron microscopy(FE-SEM)image processing of shale to realize the accurate identification and quantitative characterization of inorganic pores on the surface of high-precision images of shale with a small view.Moreover,large-view image splicing technology,combined with quantitative evaluation of minerals by scanning electron microscopy(QEMSCAN)image joint characterization technology,was used to accurately analyze the distribution characteristics of inorganic pores under different mineral components.The quantitative methods of pore characteristics of different inorganic components under the pixel information of shale were studied.The results showed that(1)the Waikato Environment for Knowledge Analysis(WEKA)machine learning model can effectively identify and extract shale mineral components and inorganic pore distribution,and the large-view FE-SEM images are representative of samples at the 200μm×200μm view scale,meeting statistical requirements and eliminating the influence of heterogeneity;(2)the pores developed by different mineral components of shale had obvious differences,indicating that the development of inorganic pores is highly correlated with the properties of shale minerals themselves;and(3)the pore-forming ability of different mineral components is calculated by the quantitative method of single component pore-forming coefficient.Chlorite showed the highest pore-forming ability,followed by(in descending order)illite,pyrite,calcite,dolomite,albite,orthoclase,quartz,and apatite.This study contributes to advancing our understanding of inorganic pore characteristics in shale.
基金This work is supported,in part,by the National Natural Science Foundation of China under grant numbers U1536206,U1405254,61772283,61602253,61672294,61502242in part,by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20150925 and BK20151530+1 种基金in part,by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundin part,by the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘The traditional information hiding methods embed the secret information by modifying the carrier,which will inevitably leave traces of modification on the carrier.In this way,it is hard to resist the detection of steganalysis algorithm.To address this problem,the concept of coverless information hiding was proposed.Coverless information hiding can effectively resist steganalysis algorithm,since it uses unmodified natural stego-carriers to represent and convey confidential information.However,the state-of-the-arts method has a low hidden capacity,which makes it less appealing.Because the pixel values of different regions of the molecular structure images of material(MSIM)are usually different,this paper proposes a novel coverless information hiding method based on MSIM,which utilizes the average value of sub-image’s pixels to represent the secret information,according to the mapping between pixel value intervals and secret information.In addition,we employ a pseudo-random label sequence that is used to determine the position of sub-images to improve the security of the method.And the histogram of the Bag of words model(BOW)is used to determine the number of subimages in the image that convey secret information.Moreover,to improve the retrieval efficiency,we built a multi-level inverted index structure.Furthermore,the proposed method can also be used for other natural images.Compared with the state-of-the-arts,experimental results and analysis manifest that our method has better performance in anti-steganalysis,security and capacity.
基金Project(2006C11200) supported by the Science and Technology Project of Zhejiang Province of China
文摘In order to improve the video quality of transmission with data loss,a spatial and temporal error concealment method was proposed,which considered both the state information of the network and the perceptual weight of the video content.The proposed method dynamically changed the reliability weight of the neighboring macroblock,which was used to conceal the lost macroblocks according to the packet loss rate of the current channel state.The perceptual weight map was utilized as side information to do weighted pixel interpolation and side-match based motion compensation for spatial and temporal error concealment,respectively.And the perceptual weight of the neighboring macroblocks was adaptively modified according to the perceptual weight of the lost macroblocks.Compared with the method used in H.264 joint model,experiment results show that the proposed method performs well both in subjective video quality and objective video quality,and increases the average peak signal-to-noise ratio(PSNR) of the whole frame by about 0.4 dB when the video bitstreams are transmitted with packets loss.
基金This work was funded by the Deanship of Scientific Research at Jouf University under grant No DSR-2021-02-0398.
文摘Image dehazing is still an open research topic that has been undergoing a lot of development,especially with the renewed interest in machine learning-based methods.A major challenge of the existing dehazing methods is the estimation of transmittance,which is the key element of haze-affected imaging models.Conventional methods are based on a set of assumptions that reduce the solution search space.However,the multiplication of these assumptions tends to restrict the solutions to particular cases that cannot account for the reality of the observed image.In this paper we reduce the number of simplified hypotheses in order to attain a more plausible and realistic solution by exploiting a priori knowledge of the ground truth in the proposed method.The proposed method relies on pixel information between the ground truth and haze image to reduce these assumptions.This is achieved by using ground truth and haze image to find the geometric-pixel information through a guided Convolution Neural Networks(CNNs)with a Parallax Attention Mechanism(PAM).It uses the differential pixel-based variance in order to estimate transmittance.The pixel variance uses local and global patches between the assumed ground truth and haze image to refine the transmission map.The transmission map is also improved based on improved Markov random field(MRF)energy functions.We used different images to test the proposed algorithm.The entropy value of the proposed method was 7.43 and 7.39,a percent increase of4.35%and5.42%,respectively,compared to the best existing results.The increment is similar in other performance quality metrics and this validate its superiority compared to other existing methods in terms of key image quality evaluation metrics.The proposed approach’s drawback,an over-reliance on real ground truth images,is also investigated.The proposed method show more details hence yields better images than those from the existing state-of-the-art-methods.
基金supported by the NSFC(61173141,61362032,U1536206, 61232016,U1405254,61373133,61502242,61572258)BK20150925+4 种基金the Natural Science Foundation of Jiangxi Province, China(20151BAB207003)the Fund of Jiangsu Engineering Center of Network Monitoring(KJR1402)the Fund of MOE Internet Innovation Platform(KJRP1403)the CICAEET fundthe PAPD fund
文摘Nowadays,many steganographic tools have been developed,and secret messages can be imperceptibly transmitted through public networks.This paper concentrates on steganalysis against spatial least significant bit(LSB) matching,which is the prototype of many advanced information hiding methods.Many existing algorithms deal with steganalysis problems by using the dependencies between adjacent pixels.From another aspect,this paper calculates the differences among pixel pairs and proves that the histogram of difference values will be smoothed by stego noises.We calculate the difference histogram characteristic function(DHCF) and deduce that the moment of DHCFs(DHCFM) will be diminished after stego bits are hidden in the image.Accordingly,we compute the DHCFMs as the discriminative features.We calibrate the features by decreasing the influence of image content on them and train support vector machine classifiers based on the calibrated features.Experimental results demonstrate that the DHCFMs calculated with nonadjacent pixels are helpful to detect stego messages hidden by LSB matching.
