The Experts below are selected from a list of 5616 Experts worldwide ranked by ideXlab platform

P M Patil - One of the best experts on this subject based on the ideXlab platform.

  • satellite image resolution enhancement using dyadic Integer Coefficients based bi orthogonal wavelet filters
    Procedia Computer Science, 2015
    Co-Authors: B D Jadhav, P M Patil
    Abstract:

    Abstract The acquisition of high satellite Imagery becomes difficult due to the instrument limitation and imperfect imaging optics. Low spatial resolution satellite images consist a lot of mixed pixels which degrade the detection and recognition performance in civil and military applications. In this paper, a satellite image resolution enhancement using Dyadic-Integer Coefficients based bi-orthogonal wavelet filters is proposed. Dyadic-Integer-Coefficient based wavelet filters are derived from the construction of a half-band polynomial. The splitting approach is used to develop the Integer-Coefficient based half-band polynomial. The possibility of these dyadic-Integer Coefficients based wavelet filters is explored in the field of image enhancement using sub-pixel image registration. The two-resolution frames are registered at a specific shift from one another to restore the resolution lost by the sensors. The discrete wavelet transform (DWT) obtained from the designed Coefficients is applied on these two low-resolution images to obtain the high resolution image. The proposed method has been tested on satellite images. The quantitative (peak signal-to-noise ratio and root mean square error) and visual results show the superiority of the proposed technique over the conventional and state-of-art image resolution enhancement techniques.

  • design of dyadic Integer Coefficients based bi orthogonal wavelet filters for image super resolution using sub pixel image registration
    ICTACT Journal on Image and Video Processing, 2014
    Co-Authors: P B Chopade, P M Patil
    Abstract:

    This paper presents image super-resolution scheme based on subpixel image registration by the design of a specific class of dyadicInteger-Coefficient based wavelet filters derived from the construction of a half-band polynomial. First, the Integer-Coefficient based halfband polynomial is designed by the splitting approach. Next, this designed half-band polynomial is factorized and assigned specific number of vanishing moments and roots to obtain the dyadic-Integer Coefficients low-pass analysis and synthesis filters. The possibility of these dyadic-Integer Coefficients based wavelet filters is explored in the field of image super-resolution using sub-pixel image registration. The two-resolution frames are registered at a specific shift from one another to restore the resolution lost by CCD array of camera. The discrete wavelet transform (DWT) obtained from the designed Coefficients is applied on these two low-resolution images to obtain the high resolution image. The developed approach is validated by comparing the quality metrics with existing filter banks.

Cong Ling - One of the best experts on this subject based on the ideXlab platform.

  • efficient Integer Coefficient search for compute and forward
    IEEE Transactions on Wireless Communications, 2016
    Co-Authors: William Liu, Cong Ling
    Abstract:

    Integer Coefficient selection is an important decoding step in the implementation of compute-and-forward (C-F) relaying scheme. Choosing the optimal Integer Coefficients in C-F has been shown to be a shortest vector problem, which is known to be NP-hard in its general form. Exhaustive search of the Integer Coefficients is only feasible in complexity for small number of users while approximation algorithms, such as Lenstra–Lenstra–Lovasz lattice reduction algorithm, only find a vector within an exponential factor of the shortest vector. An optimal deterministic algorithm was proposed for C-F by Sahraei and Gastpar specifically for the real valued channel case. In this paper, we adapt their idea to the complex valued channel and propose an efficient search algorithm to find the optimal Integer Coefficient vectors over the ring of Gaussian Integers and the ring of Eisenstein Integers. A second algorithm is then proposed that generalizes our search algorithm to the Integer-forcing multiple-input multiple-output (MIMO) C-F receiver. Performance and efficiency of the proposed algorithms are evaluated through simulations and theoretical analysis.

  • efficient Integer Coefficient search for compute and forward
    arXiv: Information Theory, 2016
    Co-Authors: William Liu, Cong Ling
    Abstract:

    Integer Coefficient selection is an important decoding step in the implementation of compute-and-forward (C-F) relaying scheme. Choosing the optimal Integer Coefficients in C-F has been shown to be a shortest vector problem (SVP) which is known to be NP hard in its general form. Exhaustive search of the Integer Coefficients is only feasible in complexity for small number of users while approximation algorithms such as Lenstra-Lenstra-Lovasz (LLL) lattice reduction algorithm only find a vector within an exponential factor of the shortest vector. An optimal deterministic algorithm was proposed for C-F by Sahraei and Gastpar specifically for the real valued channel case. In this paper, we adapt their idea to the complex valued channel and propose an efficient search algorithm to find the optimal Integer Coefficient vectors over the ring of Gaussian Integers and the ring of Eisenstein Integers. A second algorithm is then proposed that generalises our search algorithm to the Integer-Forcing MIMO C-F receiver. Performance and efficiency of the proposed algorithms are evaluated through simulations and theoretical analysis.

William Liu - One of the best experts on this subject based on the ideXlab platform.

  • efficient Integer Coefficient search for compute and forward
    IEEE Transactions on Wireless Communications, 2016
    Co-Authors: William Liu, Cong Ling
    Abstract:

    Integer Coefficient selection is an important decoding step in the implementation of compute-and-forward (C-F) relaying scheme. Choosing the optimal Integer Coefficients in C-F has been shown to be a shortest vector problem, which is known to be NP-hard in its general form. Exhaustive search of the Integer Coefficients is only feasible in complexity for small number of users while approximation algorithms, such as Lenstra–Lenstra–Lovasz lattice reduction algorithm, only find a vector within an exponential factor of the shortest vector. An optimal deterministic algorithm was proposed for C-F by Sahraei and Gastpar specifically for the real valued channel case. In this paper, we adapt their idea to the complex valued channel and propose an efficient search algorithm to find the optimal Integer Coefficient vectors over the ring of Gaussian Integers and the ring of Eisenstein Integers. A second algorithm is then proposed that generalizes our search algorithm to the Integer-forcing multiple-input multiple-output (MIMO) C-F receiver. Performance and efficiency of the proposed algorithms are evaluated through simulations and theoretical analysis.

  • efficient Integer Coefficient search for compute and forward
    arXiv: Information Theory, 2016
    Co-Authors: William Liu, Cong Ling
    Abstract:

    Integer Coefficient selection is an important decoding step in the implementation of compute-and-forward (C-F) relaying scheme. Choosing the optimal Integer Coefficients in C-F has been shown to be a shortest vector problem (SVP) which is known to be NP hard in its general form. Exhaustive search of the Integer Coefficients is only feasible in complexity for small number of users while approximation algorithms such as Lenstra-Lenstra-Lovasz (LLL) lattice reduction algorithm only find a vector within an exponential factor of the shortest vector. An optimal deterministic algorithm was proposed for C-F by Sahraei and Gastpar specifically for the real valued channel case. In this paper, we adapt their idea to the complex valued channel and propose an efficient search algorithm to find the optimal Integer Coefficient vectors over the ring of Gaussian Integers and the ring of Eisenstein Integers. A second algorithm is then proposed that generalises our search algorithm to the Integer-Forcing MIMO C-F receiver. Performance and efficiency of the proposed algorithms are evaluated through simulations and theoretical analysis.

P B Chopade - One of the best experts on this subject based on the ideXlab platform.

  • image resolution enhancement of satellite images using dyadic Integer Coefficient wavelet filter for wavelet transform
    INFORMATION TECHNOLOGY IN INDUSTRY, 2021
    Co-Authors: P B Chopade, P N Kota
    Abstract:

    In this paper, image resolution enhancements for satellite images are proposed using dyadic Integer Coefficients based wavelet filter (DICWF). We proposes a technique in which discrete wavelet transform and stationary wavelet transform using DICWF which is used to obtain a high resolution image and this image is derived from frequency subbands. The satellite images play a very vital role now days in the development of technical aspects which needs to be enhanced. These satellite images are superresolved with the help of dyadic Integer Coefficient-based wavelet filters, which reduces the hardware complexity and computational difficulties due to the rational and Integer Coefficients of these filter banks. The value of the peak signal-to-noise ratio (PSNR) of the proposed method and the resultant visual images of the proposed method show the effectiveness of this algorithm over other existing algorithms using discrete wavelet transform. Noise can be minimized by applying thresholding on different frequency subbands which obtained by the application of DICWF to the noisy, blurred input images.

  • design of dyadic Integer Coefficients based bi orthogonal wavelet filters for image super resolution using sub pixel image registration
    ICTACT Journal on Image and Video Processing, 2014
    Co-Authors: P B Chopade, P M Patil
    Abstract:

    This paper presents image super-resolution scheme based on subpixel image registration by the design of a specific class of dyadicInteger-Coefficient based wavelet filters derived from the construction of a half-band polynomial. First, the Integer-Coefficient based halfband polynomial is designed by the splitting approach. Next, this designed half-band polynomial is factorized and assigned specific number of vanishing moments and roots to obtain the dyadic-Integer Coefficients low-pass analysis and synthesis filters. The possibility of these dyadic-Integer Coefficients based wavelet filters is explored in the field of image super-resolution using sub-pixel image registration. The two-resolution frames are registered at a specific shift from one another to restore the resolution lost by CCD array of camera. The discrete wavelet transform (DWT) obtained from the designed Coefficients is applied on these two low-resolution images to obtain the high resolution image. The developed approach is validated by comparing the quality metrics with existing filter banks.

Végh, László A. - One of the best experts on this subject based on the ideXlab platform.

  • Revisiting Tardos's Framework for Linear Programming: Faster Exact Solutions using Approximate Solvers
    2020
    Co-Authors: Dadush Daniel, Natura Bento, Végh, László A.
    Abstract:

    In breakthrough work, Tardos (Oper. Res. '86) gave a proximity based framework for solving linear programming (LP) in time depending only on the constraint matrix in the bit complexity model. In Tardos's framework, one reduces solving the LP $\min \langle c,{x}\rangle$, $Ax=b$, $x \geq 0$, $A \in \mathbb{Z}^{m \times n}$, to solving $O(nm)$ LPs in $A$ having small Integer Coefficient objectives and right-hand sides using any exact LP algorithm. This gives rise to an LP algorithm in time poly$(n,m\log\Delta_A)$, where $\Delta_A$ is the largest subdeterminant of $A$. A significant extension to the real model of computation was given by Vavasis and Ye (Math. Prog. '96), giving a specialized interior point method that runs in time poly$(n,m,\log\bar\chi_A)$, depending on Stewart's $\bar{\chi}_A$, a well-studied condition number. In this work, we extend Tardos's original framework to obtain such a running time dependence. In particular, we replace the exact LP solves with approximate ones, enabling us to directly leverage the tremendous recent algorithmic progress for approximate linear programming. More precisely, we show that the fundamental "accuracy" needed to exactly solve any LP in $A$ is inverse polynomial in $n$ and $\log\bar{\chi}_A$. Plugging in the recent algorithm of van den Brand (SODA '20), our method computes an optimal primal and dual solution using ${O}(m n^{\omega+1} \log (n)\log(\bar{\chi}_A+n))$ arithmetic operations, outperforming the specialized interior point method of Vavasis and Ye and its recent improvement by Dadush et al (STOC '20). At a technical level, our framework combines together approximate LP solutions to compute exact ones, making use of constructive proximity theorems -- which bound the distance between solutions of "nearby" LPs -- to keep the required accuracy low

  • Revisiting Tardos's framework for linear programming: faster exact solutions using approximate solvers
    IEEE Computer Society, 2020
    Co-Authors: Dadush Daniel, Natura Bento, Végh, László A.
    Abstract:

    In breakthrough work, Tardos (Oper. Res. ’86) gave a proximity based framework for solving linear programming (LP) in time depending only on the constraint matrix in the bit complexity model. In Tardos’s framework, one reduces solving the LP min⟨c, x⟩, Ax = b, x ≥ 0, A ∈ Z m×n, to solving O(nm) LPs in A having small Integer Coefficient objectives and right-hand sides using any exact LP algorithm. This gives rise to an LP algorithm in time poly(n, m log ∆A), where ∆A is the largest subdeterminant of A. A significant extension to the real model of computation was given by Vavasis and Ye (Math. Prog. ’96), giving a specialized interior point method that runs in time poly(n, m, log ¯χA), depending on Stewart’s χ¯A, a well-studied condition number. In this work, we extend Tardos’s original framework to obtain such a running time dependence. In particular, we replace the exact LP solves with approximate ones, enabling us to directly leverage the tremendous recent algorithmic progress for approximate linear programming. More precisely, we show that the fundamental “accuracy” needed to exactly solve any LP in A is inverse polynomial in n and log ¯χA. Plugging in the recent algorithm of van den Brand (SODA ’20), our method computes an optimal primal and dual solution using O(mnω+1+o(1) log( ¯χA + n)) arithmetic operations, outperforming the specialized interior point method of Vavasis and Ye and its recent improvement by Dadush et al (STOC ’20). By applying the preprocessing algorithm of the latter paper, the dependence can also be reduced from ¯χA to ¯χ ∗ A, the minimum value of ¯χAD attainable via column rescalings. Our framework is applicable to achieve the poly(n, m, log ¯χ ∗ A) bound using essentially any weakly polynomial LP algorithm, such as the ellipsoid method. At a technical level, our framework combines together approximate LP solutions to compute exact ones, making use of constructive proximity theorems—which bound the distance between solutions of “nearby” LPs—to keep the required accuracy low