scholarly journals Bivariate Hahn moments for image reconstruction

2014 ◽  
Vol 24 (2) ◽  
pp. 417-428 ◽  
Author(s):  
Haiyong Wu ◽  
Senlin Yan
Keyword(s):  
Image Reconstruction ◽  
Numerical Stability ◽  
Parameter Selection ◽  
Experimental Results ◽  
Grayscale Image ◽  
Orthogonal Moments ◽  
Binary Images ◽  
Hahn Polynomials ◽  
Discrete Orthogonal ◽  
Computational Aspects

Abstract This paper presents a new set of bivariate discrete orthogonal moments which are based on bivariate Hahn polynomials with non-separable basis. The polynomials are scaled to ensure numerical stability. Their computational aspects are discussed in detail. The principle of parameter selection is established by analyzing several plots of polynomials with different kinds of parameters. Appropriate parameters of binary images and a grayscale image are obtained through experimental results. The performance of the proposed moments in describing images is investigated through several image reconstruction experiments, including noisy and noise-free conditions. Comparisons with existing discrete orthogonal moments are also presented. The experimental results show that the proposed moments outperform slightly separable Hahn moments for higher orders.

A numerical recipe for accurate image reconstruction from discrete orthogonal moments

2007 ◽  
Vol 40 (2) ◽  
pp. 659-669 ◽  
Author(s):  
Bulent Bayraktar ◽  
Tytus Bernas ◽  
J. Paul Robinson ◽  
Bartek Rajwa
Keyword(s):  
Image Reconstruction ◽  
Orthogonal Moments ◽  
Discrete Orthogonal

EXACT GRAYSCALE IMAGE RECONSTRUCTION FROM PROJECTIONS

2006 ◽  
Vol 20 (05) ◽  
pp. 711-725 ◽  
Author(s):  
ANASTASIOS L. KESIDIS ◽  
NIKOS PAPAMARKOS
Keyword(s):  
Image Reconstruction ◽  
Experimental Results ◽  
Grayscale Image ◽  
Original Image ◽  
Reconstruction Process ◽  
Exact Reconstruction ◽  
Characteristic Sample ◽  
Image Projections ◽  
Image Reconstruction From Projections ◽  
Two Parameters

This paper proposes a new method for the exact reconstruction of gray-scale images from projections. The image projections construct an accumulator array, which is used afterwards to reconstruct the original grayscale image by applying the proposed decomposition algorithm. The proposed method determines the number of projections and the number of rays in each projection that are required in order to achieve the reconstruction. These two parameters also define the dimensions of the accumulator array. Using an accumulator array with proper dimensions ensures that there is always a unique characteristic sample for each pixel, which is used during the reconstruction process to extract the pixel's grayscale value. During the reconstruction phase, the sinusoidal contribution of each pixel is removed from the accumulator array. At the end of the decomposition process the accumulator array becomes empty and the original image is exactly reconstructed. The experimental results confirm the robustness and efficiency of the proposed method.

scholarly journals Invariant Image Representation Using Novel Fractional-Order Polar Harmonic Fourier Moments

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1544
Author(s):  
Chunpeng Wang ◽  
Hongling Gao ◽  
Meihong Yang ◽  
Jian Li ◽  
Bin Ma ◽  
...  
Keyword(s):  
Image Reconstruction ◽  
Fractional Order ◽  
Continuous Functions ◽  
Kernel Functions ◽  
Superior Performance ◽  
Image Description ◽  
Orthogonal Moments ◽  
Integer Order ◽  
Geometric Invariance ◽  
Order Continuous

Continuous orthogonal moments, for which continuous functions are used as kernel functions, are invariant to rotation and scaling, and they have been greatly developed over the recent years. Among continuous orthogonal moments, polar harmonic Fourier moments (PHFMs) have superior performance and strong image description ability. In order to improve the performance of PHFMs in noise resistance and image reconstruction, PHFMs, which can only take integer numbers, are extended to fractional-order polar harmonic Fourier moments (FrPHFMs) in this paper. Firstly, the radial polynomials of integer-order PHFMs are modified to obtain fractional-order radial polynomials, and FrPHFMs are constructed based on the fractional-order radial polynomials; subsequently, the strong reconstruction ability, orthogonality, and geometric invariance of the proposed FrPHFMs are proven; and, finally, the performance of the proposed FrPHFMs is compared with that of integer-order PHFMs, fractional-order radial harmonic Fourier moments (FrRHFMs), fractional-order polar harmonic transforms (FrPHTs), and fractional-order Zernike moments (FrZMs). The experimental results show that the FrPHFMs constructed in this paper are superior to integer-order PHFMs and other fractional-order continuous orthogonal moments in terms of performance in image reconstruction and object recognition, as well as that the proposed FrPHFMs have strong image description ability and good stability.

Image Reconstruction on Quincunx Patterned Green Channel

2013 ◽  
Vol 717 ◽  
pp. 493-496
Author(s):  
Gwang Gil Jeon
Keyword(s):  
Image Reconstruction ◽  
Least Squares ◽  
Least Squares Method ◽  
Interpolation Method ◽  
Experimental Results ◽  
Color Filter ◽  
Image Artifacts ◽  
Green Channel ◽  
Filter Array ◽  
Single Sensor

This paper addresses the issue of the quincunx patterned green channel interpolation method that is obtained by single sensor cameras. Our goal is to reconstruct the green channel in Bayer color filter array (CFA) data. We present a new filter-based method for the reduction of image artifacts in green channel. To reconstruct green channel, we trained a filter using least squares method. Experimental results confirm the effectiveness of the proposed method. Compared to other bilinear and bicubic filters, the improvement in quality has been achieved.

Partial 3D Image Reconstruction by Cuboids Using Stable Computation of Hahn Polynomials

2021 ◽  
pp. 831-842
Author(s):  
Mohamed Amine Tahiri ◽  
Hicham Karmouni ◽  
Ahmed Tahiri ◽  
Mhamed Sayyouri ◽  
Hassan Qjidaa
Keyword(s):  
Image Reconstruction ◽  
3D Image ◽  
3D Image Reconstruction ◽  
Hahn Polynomials ◽  
Stable Computation

scholarly journals Adaptive parameter selection for weighted-TV image reconstruction problems

2020 ◽  
Vol 1476 ◽  
pp. 012003
Author(s):  
Luca Calatroni ◽  
Alessandro Lanza ◽  
Monica Pragliola ◽  
Fiorella Sgallari
Keyword(s):  
Image Reconstruction ◽  
Parameter Selection ◽  
Adaptive Parameter ◽  
Selection For

scholarly journals A New Error Concealment Algorithm Based on Directional Texture Synthesis

2012 ◽  
Vol 6-7 ◽  
pp. 309-314
Author(s):  
Jiang Xin Zhang ◽  
Jin Xie
Keyword(s):  
Image Reconstruction ◽  
Confidence Level ◽  
Error Concealment ◽  
Texture Synthesis ◽  
Experimental Results ◽  
Interpolation Algorithm ◽  
Structure Information ◽  
Video Images ◽  
Directional Texture

In this paper, we propose a novel directional texture synthesis based error concealment algorithm to recover damaged video images. It uses the confidence level and structure information to calculate the priority of patch, which contributes to improve the ability to select the best matching block when the damaged area is very large. The JM86 model of H.264 standard is used to evaluate the algorithm. And experimental results show that our algorithm achieved a better image reconstruction results than the improved Multi-directional texture interpolation algorithm, with 1.2 to 1.4dB gain in PSNR and 0.5 percent to 1 percent gain in SSIM.

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