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 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.

Wavelet Transforms of Image Reconstruction Based on Compressed Sampling

2011 ◽  
Vol 58-60 ◽  
pp. 1920-1925
Author(s):  
Yan Wei Wang ◽  
Hui Li Yu
Keyword(s):  
Image Reconstruction ◽  
Image Compression ◽  
Compressive Sensing ◽  
Wavelet Transforms ◽  
Sparse Matrix ◽  
Experimental Results ◽  
Original Image ◽  
Compressed Sampling ◽  
Image Compressing

A compressive sensing technique for image signal to cope with image compression and restoration is adopted in this paper. First of all wavelet transforms method is applied in image compressing to preserve the constructive, Secondly, sparse matrix is available by required wavelet ratio. Thirdly, the compressing image is used to restoration the original image. Experimental results show that the proposed algorithm is effective and compares favorably with existing techniques.

scholarly journals A New Approach to Image Reconstruction from Projections Using a Recurrent Neural Network

2008 ◽  
Vol 18 (2) ◽  
pp. 147-157 ◽  
Author(s):  
Robert Cierniak
Keyword(s):  
Neural Network ◽  
Image Reconstruction ◽  
Recurrent Neural Network ◽  
Reconstruction Algorithm ◽  
Neural Network Approach ◽  
Reconstruction Process ◽  
New Approach ◽  
The Neural Network ◽  
Image Reconstruction From Projections ◽  
Parallel Geometry

A New Approach to Image Reconstruction from Projections Using a Recurrent Neural NetworkA new neural network approach to image reconstruction from projections considering the parallel geometry of the scanner is presented. To solve this key problem in computed tomography, a special recurrent neural network is proposed. The reconstruction process is performed during the minimization of the energy function in this network. The performed computer simulations show that the neural network reconstruction algorithm designed to work in this way outperforms conventional methods in the obtained image quality.

A CRYPTO SIGNATURE SCHEME FOR IMAGE AUTHENTICATION OVER WIRELESS CHANNEL

2005 ◽  
Vol 05 (01) ◽  
pp. 135-148 ◽  
Author(s):  
QIBIN SUN ◽  
SHUIMING YE ◽  
CHING-YUNG LIN ◽  
SHIH-FU CHANG
Keyword(s):  
Packet Loss ◽  
Digital Signature ◽  
Digital Images ◽  
Image Authentication ◽  
Wireless Channel ◽  
Experimental Results ◽  
Signature Scheme ◽  
Original Image ◽  
Degraded Images ◽  
Block Shuffling

With the ambient use of digital images and the increasing concern on their integrity and originality, consumers are facing an emergent need of authenticating degraded images despite lossy compression and packet loss. In this paper, we propose a scheme to meet this need by incorporating watermarking solution into traditional cryptographic signature scheme to make the digital signatures robust to these image degradations. Due to the unpredictable degradations, the pre-processing and block shuffling techniques are applied onto the image at the signing end to stabilize the feature extracted at the verification end. The proposed approach is compatible with traditional cryptographic signature scheme except that the original image needs to be watermarked in order to guarantee the robustness of its derived digital signature. We demonstrate the effectiveness of this proposed scheme through practical experimental results.

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.

Blind Digital Image Watermarking Algorithm Based on the Chain Code

2012 ◽  
Vol 546-547 ◽  
pp. 410-415
Author(s):  
Chun Ge Tang ◽  
Tie Sheng Fan ◽  
Lei Liu ◽  
Zhi Hui Li
Keyword(s):  
Image Quality ◽  
Digital Watermarking ◽  
Digital Image ◽  
Image Watermarking ◽  
Boundary Representation ◽  
Experimental Results ◽  
Digital Image Watermarking ◽  
Original Image ◽  
Chain Code ◽  
Decision Method

A new blind digital watermarking algorithm based on the chain code is proposed. The chain code is obtained by the characteristics of the original image -the edge contour. The feather can reflect the overall correlation of the vector image, and chain code expression can significantly reduce the boundary representation of the amount of data required. For the watermarking embedding, the original vector image is divided into sub-block images, and two bits of the watermarking information are embedded into sub-block images repeatedly by quantization. For watermarking extracting, the majority decision method is employed to determine the size of the extracted watermark. Experimental results show that the image quality is not significantly lowered after watermarking. The algorithm can resist the basic conventional attacks and has good robustness on the shear attacks.

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