The Experts below are selected from a list of 5583 Experts worldwide ranked by ideXlab platform
T Q Nguyen - One of the best experts on this subject based on the ideXlab platform.
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markov random field model based edge directed Image interpolation
IEEE Transactions on Image Processing, 2008Co-Authors: T Q NguyenAbstract:This paper presents an edge-directed Image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. In opposite to explicit edge directions, the local edge directions are indicated by length-16 weighting vectors. Implicitly, the weighting vectors are used to formulate geometric regularity (GR) constraint (smoothness along edges and sharpness across edges) and the GR constraint is imposed on the Interpolated Image through the Markov random field (MRF) model. Furthermore, under the maximum a posteriori-MRF framework, the desired Interpolated Image corresponds to the minimal energy state of a 2-D random field given the low-resolution Image. Simulated annealing methods are used to search for the minimal energy state from the state space. To lower the computational complexity of MRF, a single-pass implementation is designed, which performs nearly as well as the iterative optimization. Simulation results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity. Compared to traditional methods and other edge-directed interpolation methods, the proposed method improves the subjective quality of the Interpolated edges while maintaining a high PSNR level.
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markov random field model based edge directed Image interpolation
International Conference on Image Processing, 2007Co-Authors: T Q NguyenAbstract:This paper presents an edge-directed Image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. Consequently, the local edge directions are represented by length-16 vectors, which are denoted as weight vectors. The weight vectors are used to formulate geometric regularity constraint, which is imposed on the Interpolated Image through the Markov Random Field (MRF) model. Furthermore, the interpolation problem is formulated as a Maximum A Posterior (MAP)-MRF problem and, under the MAP-MRF framework, the desired Interpolated Image corresponds to the minimal energy state of a two-dimensional random held. Simulated Annealing method is used to search for the minimal energy state from a reasonable large state space. Simulation and comparison results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity.
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ICIP (2) - Markov Random Field Model-Based Edge-Directed Image Interpolation
2007 IEEE International Conference on Image Processing, 2007Co-Authors: T Q NguyenAbstract:This paper presents an edge-directed Image interpolation algorithm. In the proposed algorithm, the edge directions are implicitly estimated with a statistical-based approach. Consequently, the local edge directions are represented by length-16 vectors, which are denoted as weight vectors. The weight vectors are used to formulate geometric regularity constraint, which is imposed on the Interpolated Image through the Markov Random Field (MRF) model. Furthermore, the interpolation problem is formulated as a Maximum A Posterior (MAP)-MRF problem and, under the MAP-MRF framework, the desired Interpolated Image corresponds to the minimal energy state of a two-dimensional random held. Simulated Annealing method is used to search for the minimal energy state from a reasonable large state space. Simulation and comparison results show that the proposed MRF model-based edge-directed interpolation method produces edges with strong geometric regularity.
Harsh K Verma - One of the best experts on this subject based on the ideXlab platform.
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A Reversible Data Hiding Scheme for Interpolated Images Based on Pixel Intensity Range
Multimedia Tools and Applications, 2020Co-Authors: Aruna Malik, Geeta Sikka, Harsh K VermaAbstract:In this paper, we propose a novel interpolation and a new reversible data hiding scheme for upscaling the original Image and hiding secret data into the upscaled/Interpolated Image. This data hiding scheme considers the characteristics of the human visual system while embedding the secret data so that the existence of the secret data is not detected even after embedding a large amount of secret data. The proposed hiding scheme first divides pixel intensity ranges into groups and then adaptively embeds the secret data bits into the pixels based on the pixel intensity values. Therefore, the proposed scheme is able to maintain the visual quality of the stego-Image. Experimental results show that the achieved PSNR by the proposed interpolation method is more than 30 dB for all the test Images. Further, the results prove that the proposed data hiding scheme has superior performance than all the existing interpolation-based data hiding schemes.
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An Image interpolation based reversible data hiding scheme using pixel value adjusting feature
Multimedia Tools and Applications, 2017Co-Authors: Aruna Malik, Geeta Sikka, Harsh K VermaAbstract:In this paper, we propose an Image interpolation based reversible data hiding scheme using pixel value adjusting feature. This scheme consists of two phases, namely: Image interpolation and data hiding. In order to interpolate the original Image, we propose a new Image interpolation method which is based on the existing neighbor mean interpolation method. Our interpolation method takes into account all the neighboring pixels like the NMI method. However, it uses different weight-age as per their proximity. Thus, it provides the better quality Interpolated Image. In case of data hiding phase, secret data is embedded in the Interpolated pixels in two passes. In the first pass, it embeds the secret data into the odd valued pixels and then in the second pass, the even valued pixels are used to embed the secret data. To ensure the reversibility of the proposed scheme, the location map is constructed for every pass. Basically, the proposed scheme only increases/decreases the pixel values during data hiding phase, which improves the performance of the proposed scheme in terms of computation complexity. Experimentally, our scheme is superior to the existing scheme in terms of data hiding capacity, Image quality and computation complexity.
Thomas Moeslund - One of the best experts on this subject based on the ideXlab platform.
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A new low-complexity patch-based Image super-resolution
IET Computer Vision, 2017Co-Authors: Pejman Rasti, Kamal Nasrollahi, Olga Orlova, Gert Tamberg, Thomas MoeslundAbstract:In this study, a novel single Image super-resolution (SR) method, which uses a generated dictionary from pairs of high-resolution (HR) Images and their corresponding low-resolution (LR) representations, is proposed. First, HR and LR dictionaries are created by dividing HR and LR Images into patches Afterwards, when performing SR, the distance between every patch of the input LR Image and those of available LR patches in the LR dictionary are calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary will be passed through an illumination enhancement process resulting in consistency of illumination between neighbour patches. This process is applied to all patches of the LR Image. Finally, in order to remove the blocking effect caused by merging the patches, an average of the obtained HR Image and the Interpolated Image is calculated. Furthermore, it is shown that the stabe of dictionaries is reducible to a great degree. The speed of the system is improved by 62.5%. The quantitative and qualitative analyses of the experimental results show the superiority of the proposed technique over the conventional and state-of-the-art methods.
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Reducible dictionaries for single Image super-resolution based on patch matching and mean shifting
Journal of Electronic Imaging, 2017Co-Authors: Pejman Rasti, Kamal Nasrollahi, Olga Orlova, Gert Tamberg, Thomas MoeslundAbstract:A single-Image super-resolution (SR) method is proposed. The proposed method uses a generated dictionary from pairs of high resolution (HR) Images and their corresponding low resolution (LR) representations. First, HR Images and the corresponding LR ones are divided into patches of HR and LR, respectively, and then they are collected into separate dictionaries. Afterward, when performing SR, the distance between every patch of the input LR Image and those of available LR patches in the LR dictionary is calculated. The minimum distance between the input LR patch and those in the LR dictionary is taken, and its counterpart from the HR dictionary is passed through an illumination enhancement process. By this technique, the noticeable change of illumination between neighbor patches in the super-resolved Image is significantly reduced. The enhanced HR patch represents the HR patch of the super-resolved Image. Finally, to remove the blocking effect caused by merging the patches, an average of the obtained HR Image and the Interpolated Image obtained using bicubic interpolation is calculated. The quantitative and qualitative analyses show the superiority of the proposed technique over the conventional and state-of-art methods.
King Ngi Ngan - One of the best experts on this subject based on the ideXlab platform.
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weighted adaptive lifting based wavelet transform for Image coding
IEEE Transactions on Image Processing, 2008Co-Authors: King Ngi NganAbstract:In this paper, a new weighted adaptive lifting (WAL)-based wavelet transform is presented. The proposed WAL approach is designed to solve the problems existing in the previous adaptive directional lifting (ADL) approach, such as mismatch between the predict and update steps, interpolation favoring only horizontal or vertical direction, and invariant interpolation filter coefficients for all Images. The main contribution of the proposed approach consists of two parts: one is the improved weighted lifting, which maintains the consistency between the predict and update steps as far as possible and preserves the perfect reconstruction at the same time; another is the directional adaptive interpolation, which improves the orientation property of the Interpolated Image and adapts to statistical property of each Image. Experimental results show that the proposed WAL-based wavelet transform for Image coding outperforms the conventional lifting-based wavelet transform up to 3.06 dB in PSNR and significant improvement in subjective quality is also observed. Compared with the ADL-based wavelet transform, up to 1.22-dB improvement in PSNR is reported.
Aruna Malik - One of the best experts on this subject based on the ideXlab platform.
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A Reversible Data Hiding Scheme for Interpolated Images Based on Pixel Intensity Range
Multimedia Tools and Applications, 2020Co-Authors: Aruna Malik, Geeta Sikka, Harsh K VermaAbstract:In this paper, we propose a novel interpolation and a new reversible data hiding scheme for upscaling the original Image and hiding secret data into the upscaled/Interpolated Image. This data hiding scheme considers the characteristics of the human visual system while embedding the secret data so that the existence of the secret data is not detected even after embedding a large amount of secret data. The proposed hiding scheme first divides pixel intensity ranges into groups and then adaptively embeds the secret data bits into the pixels based on the pixel intensity values. Therefore, the proposed scheme is able to maintain the visual quality of the stego-Image. Experimental results show that the achieved PSNR by the proposed interpolation method is more than 30 dB for all the test Images. Further, the results prove that the proposed data hiding scheme has superior performance than all the existing interpolation-based data hiding schemes.
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An Image interpolation based reversible data hiding scheme using pixel value adjusting feature
Multimedia Tools and Applications, 2017Co-Authors: Aruna Malik, Geeta Sikka, Harsh K VermaAbstract:In this paper, we propose an Image interpolation based reversible data hiding scheme using pixel value adjusting feature. This scheme consists of two phases, namely: Image interpolation and data hiding. In order to interpolate the original Image, we propose a new Image interpolation method which is based on the existing neighbor mean interpolation method. Our interpolation method takes into account all the neighboring pixels like the NMI method. However, it uses different weight-age as per their proximity. Thus, it provides the better quality Interpolated Image. In case of data hiding phase, secret data is embedded in the Interpolated pixels in two passes. In the first pass, it embeds the secret data into the odd valued pixels and then in the second pass, the even valued pixels are used to embed the secret data. To ensure the reversibility of the proposed scheme, the location map is constructed for every pass. Basically, the proposed scheme only increases/decreases the pixel values during data hiding phase, which improves the performance of the proposed scheme in terms of computation complexity. Experimentally, our scheme is superior to the existing scheme in terms of data hiding capacity, Image quality and computation complexity.
