scholarly journals A novel Image Security Protection Method Based on DCT Compression Theory and Hyper-chaotic Mapping

2021 ◽  
Vol 2066 (1) ◽  
pp. 012011
Author(s):  
Xianglian Xue ◽  
Haiyan Jin
Keyword(s):  
Low Frequency ◽  
Inverse Transformation ◽  
Image Security ◽  
Chaotic Mapping ◽  
Protection Method ◽  
Inverse Quantization ◽  
Dct Transformation ◽  
Ac Coefficient ◽  
High Frequency Ac ◽  
Dc Coefficient

Abstract This paper studies the current situation of image compression encryption and analyzes the influence of low frequency (DC coefficient) and high frequency (AC coefficient) on image structure in DCT transformation. Based on this, a novel image security protection method based on DCT compression theory and hyper-chaotic mapping is proposed. First, the position of the pixel of the original image is disturbed, and converts the image from spatial domain into frequency domain by the two-dimensional DCT transformation and quantization. Second, change the pixel values by modifying the values of the sign bit of AC coefficient and DC coefficient. At last, the encrypted image is obtained by carrying out inverse quantization, inverse transformation and reverse operation by bit.

A remote sensing image enhancement method using mean filter and unsharp masking in non-subsampled contourlet transform domain

2016 ◽  
Vol 39 (2) ◽  
pp. 183-193 ◽  
Author(s):  
Lu Liu ◽  
Zhenhong Jia ◽  
Jie Yang ◽  
Nikola Kasabov
Keyword(s):  
Remote Sensing ◽  
Image Enhancement ◽  
High Frequency ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Contourlet Transform ◽  
Inverse Transformation ◽  
Unsharp Masking ◽  
Mean Filter ◽  
Enhancement Method

The intelligibility of an image can be influenced by the pseudo-Gibbs phenomenon, a small dynamic range, low-contrast, blurred edge and noise pollution that occurs in the process of image enhancement. A new remote sensing image enhancement method using mean filter and unsharp masking methods based on non-subsampled contourlet transform (NSCT) in the scope for greyscale images is proposed in this paper. First, the initial image is decomposed into the NSCT domain with a low-frequency sub-band and several high-frequency sub-bands. Secondly, linear transformation is adopted for the coefficients of the low-frequency sub-band. The mean filter is used for the coefficients of the first high-frequency sub-band. Then, all sub-bands were reconstructed into spatial domains using the inverse transformation of NSCT. Finally, unsharp masking was used to enhance the details of the reconstructed image. The experimental results show that the proposed method is superior to other methods in improving image definition, image contrast and enhancing image edges.

Method of Footprint Image Stitching Based on Multiscale SIFT Feature Matching

2013 ◽  
Vol 303-306 ◽  
pp. 1056-1059
Author(s):  
Sen Wang ◽  
Yin Hui Zhang ◽  
Zhong Hai Shi ◽  
Zi Fen He
Keyword(s):  
Wavelet Transform ◽  
Feature Matching ◽  
Wavelet Coefficient ◽  
Low Frequency ◽  
Original Method ◽  
Inverse Transformation ◽  
Image Stitching ◽  
Image Splicing ◽  
Sift Feature ◽  
Matching Pair

The image stitching method is widely used into the suspect's footprint information extraction. In order to improve the image detail and the matching precision, the Footprint map image stitching method which is based on the wavelet transform and the SIFT feature matching is put forward. The wavelet transform in this method is perform based on the pretreatment of image, move the low frequency wavelet coefficient to zero, adjusting thresholds of the high frequency wavelet coefficient and inverse transformation, then, use the SIFT to extract and match the key-points of the processed images. For the error matching pair of coarse match, you can use the RANSAC to filter them out. This article demonstrates its advantage through to the original image splicing comparisons. The experimental results show that the method display more clear detail and the precision of matching than the original method.

NSCT-Based Multi-Sensor Image Fusion Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 3212-3216
Author(s):  
Hai Feng Tan ◽  
Wen Jie Zhao ◽  
De Jun Li ◽  
Tian Wen Luo
Keyword(s):  
Image Fusion ◽  
Average Method ◽  
Low Frequency ◽  
Weighted Average ◽  
Inverse Transformation ◽  
Fusion Image ◽  
Fusion Algorithm ◽  
Regional Energy ◽  
Focus Image ◽  
Quantitative Indicators

Against the defects that the favoritism method and average method in the multi-sensor image fusion are apt to impair the image contrast, an image fusion algorithm based on NSCT is proposed. Firstly, this algorithm applied NSCT to the rectified multi-sensor images from the same scene, then different fusion strategies were adopted to fuse the low-frequency and high-frequency directional sub-band coefficients respectively: regional energy adaptive weighted method was used for low-frequency sub-band coefficient; the directional sub-band coefficient adopted a regional-energy-matching program that combined weighted average method and selection method. Finally, the fusion image was obtained by NSCT inverse transformation. Experiments were conducted to IR and visible light image and multi-focus image respectively. And the fusion image was evaluated objectively. The experimental results show that the fusion image obtained through this algorithm has better subjective visual effects and objective quantitative indicators. It is also superior to the traditional fusion method.

scholarly journals Enhanced Median Flow Tracker Based on Photometric Correction for Videos with Abrupt Changing Illumination

2019 ◽  
Vol 17 (2) ◽  
pp. 264-271
Author(s):  
Asha Narayana ◽  
Narasimhadhan Venkata
Keyword(s):  
Object Tracking ◽  
Low Frequency ◽  
Fixed Number ◽  
Video Frame ◽  
Visual Object ◽  
Illumination Variation ◽  
Optical Flow Method ◽  
Lighting Conditions ◽  
Dct Coefficients ◽  
Dc Coefficient

Object tracking is a fundamental task in video surveillance, human-computer interaction and activity analysis. One of the common challenges in visual object tracking is illumination variation. A large number of methods for tracking have been proposed over the recent years, and median flow tracker is one of them which can handle various challenges. Median flow tracker is designed to track an object using Lucas-Kanade optical flow method which is sensitive to illumination variation, hence fails when sudden illumination changes occur between the frames. In this paper, we propose an enhanced median flow tracker to achieve an illumination invariance to abruptly varying lighting conditions. In this approach, illumination variation is compensated by modifying the Discrete Cosine Transform (DCT) coefficients of an image in the logarithmic domain. The illumination variations are mainly reflected in the low-frequency coefficients of an image. Therefore, a fixed number of DCT coefficients are ignored. Moreover, the Discrete Cosine (DC) coefficient is maintained almost constant all through the video based on entropy difference to minimize the sudden variations of lighting impacts. In addition, each video frame is enhanced by employing pixel transformation technique that improves the contrast of dull images based on probability distribution of pixels. The proposed scheme can effectively handle the gradual and abrupt changes in the illumination of the object. The experiments are conducted on fast-changing illumination videos, and results show that the proposed method improves median flow tracker with outperforming accuracy compared to the state-of-the-art trackers

scholarly journals An Image Fusion Method Based on Curvelet Transform and Guided Filter Enhancement

2020 ◽  
Vol 2020 ◽  
pp. 1-8 ◽  
Author(s):  
Hui Zhang ◽  
Xu Ma ◽  
Yanshan Tian
Keyword(s):  
Image Fusion ◽  
Low Frequency ◽  
Curvelet Transform ◽  
Night Vision ◽  
Guided Filter ◽  
Source Image ◽  
Inverse Transformation ◽  
Fusion Method ◽  
Visible Image ◽  
Coupled Neural Networks

In order to improve the clarity of image fusion and solve the problem that the image fusion effect is affected by the illumination and weather of visible light, a fusion method of infrared and visible images for night-vision context enhancement is proposed. First, a guided filter is used to enhance the details of the visible image. Then, the enhanced visible and infrared images are decomposed by the curvelet transform. The improved sparse representation is used to fuse the low-frequency part, while the high-frequency part is fused with the parametric adaptation pulse-coupled neural networks. Finally, the fusion result is obtained by inverse transformation of the curvelet transform. The experimental results show that the proposed method has good performance in detail processing, edge protection, and source image information.

scholarly journals A Novel Two-step Distance Protection for Flexible HVDC Transmission Lines

2021 ◽  
Vol 256 ◽  
pp. 02017
Author(s):  
Zeya Fang ◽  
Minghao Wen ◽  
Junchao Zheng ◽  
Minghao Wen
Keyword(s):  
Transmission Lines ◽  
Low Frequency ◽  
Lumped Parameter Model ◽  
Distance Protection ◽  
Backup Protection ◽  
Hvdc Transmission ◽  
Protection Method ◽  
Dc Line ◽  
Hvdc Line ◽  
Step Distance

DC line fault is one of the key problems that must be solved in a flexible HVDC system. During quite a long time between the existing main protection and backup protection of the HVDC line, there is no line protection method to detect the fault, which may lead the protection at the AC side to act before the backup protection of the DC line. To solve the problem, a novel two-step distance protection for flexible HVDC lines is proposed in this manuscript. Firstly, based on the uniform distributed parameter model, the equivalent lumped parameter model of the HVDC transmission line at low frequency is analyzed. Secondly, according to the time domain differential equation and the least squares algorithm, novel distance protection based on the iterative calculation of fault distance is proposed, which can eliminate the influence of distributed capacitive current and improve the precision of calculation. To improve the rapidity and reliability of the distance protection, low pass filters with two different cut-off frequencies are used to process the electrical quantities. Finally, simulation results show that the proposed distance protection can respond to metallic poleto-ground faults and pole-to-pole faults rapidly and reliably.

Objective Recognition Algorithm of Remote Sensing Image Based on Wavelet Transform

2013 ◽  
Vol 385-386 ◽  
pp. 1402-1406
Author(s):  
Ben Tu Li ◽  
Zhi Chao Yuan
Keyword(s):  
Template Matching ◽  
Low Frequency ◽  
Remote Sensing Image ◽  
Recognition Algorithm ◽  
Inverse Transformation ◽  
Orthogonal Wavelet ◽  
A Algorithm ◽  
Matching Algorithm ◽  
Noise Interference ◽  
Accurate Position

The traditional template matching algorithm which has a high time complexity, is susceptible to noise. This paper proposed a algorithm which based on the features of continuous orthogonal wavelet Daubechies, decomposed images to be identified to multiple layers using wavelet tranformation. In order to get matching position, we select matching template in low-frequency images. Then get matched position in higher layers after doing inverse transformation to low-frequency image. Finally, accurate position of matching template will get in original images. The algorithm not only can reduce the searching time when images are matched, but also can filter out a certain amount of noise, and so reduce the noise interference.

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