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.

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 Fourth-Order Anisotropic Diffusion for Inpainting and Image Compression

2021 ◽  
pp. 99-124
Author(s):  
Ikram Jumakulyyev ◽  
Thomas Schultz
Keyword(s):  
Image Compression ◽  
Fourth Order ◽  
Diffusion Tensor ◽  
Second Order ◽  
Experimental Results ◽  
Small Subset ◽  
Order Method ◽  
Original Image ◽  
Fourth Order Method ◽  
Diffusivity Function

AbstractEdge-enhancing diffusion (EED) can reconstruct a close approximation of an original image from a small subset of its pixels. This makes it an attractive foundation for PDE based image compression. In this work, we generalize second-order EED to a fourth-order counterpart. It involves a fourth-order diffusion tensor that is constructed from the regularized image gradient in a similar way as in traditional second-order EED, permitting diffusion along edges, while applying a non-linear diffusivity function across them. We show that our fourth-order diffusion tensor formalism provides a unifying framework for all previous anisotropic fourth-order diffusion based methods, and that it provides additional flexibility. We achieve an efficient implementation using a fast semi-iterative scheme. Experimental results on natural and medical images suggest that our novel fourth-order method produces more accurate reconstructions compared to the existing second-order EED.

scholarly journals Compressive Sensing Based Sampling and Reconstruction for Wireless Sensor Array Network

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Ming Yin ◽  
Kai Yu ◽  
Zhi Wang
Keyword(s):  
Compressive Sensing ◽  
Sensor Array ◽  
Superior Performance ◽  
Wireless Sensor ◽  
Prototype System ◽  
Reconstruction Method ◽  
Compressed Sampling ◽  
Data Volume ◽  
Simulation Results ◽  
Sparsity Structure

For low-power wireless systems, transmission data volume is a key property, which influences the energy cost and time delay of transmission. In this paper, we introduce compressive sensing to propose a compressed sampling and collaborative reconstruction framework, which enables real-time direction of arrival estimation for wireless sensor array network. In sampling part, random compressed sampling and 1-bit sampling are utilized to reduce sample data volume while making little extra requirement for hardware. In reconstruction part, collaborative reconstruction method is proposed by exploiting similar sparsity structure of acoustic signal from nodes in the same array. Simulation results show that proposed framework can reach similar performances as conventional DoA methods while requiring less than 15% of transmission bandwidth. Also the proposed framework is compared with some data compression algorithms. While simulation results show framework’s superior performance, field experiment data from a prototype system is presented to validate the results.

scholarly journals Fast Transforms in Image Processing: Compression, Restoration, and Resampling

2014 ◽  
Vol 2014 ◽  
pp. 1-23 ◽  
Author(s):  
Leonid P. Yaroslavsky
Keyword(s):  
Image Processing ◽  
Image Compression ◽  
Image Restoration ◽  
Compressive Sensing ◽  
Adaptive Filters ◽  
Processing Methods ◽  
Fast Transforms ◽  
Image Transforms

Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

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.

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