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11th IEEE Annual International conference on Nano/Micro Engineered and Molecular systems, NEMS 2016

作者： Li, xiuyuan Zhao, Yulong Hu, Tengjiang Zhang, Qi Li, Yingxue State Key Laboratory for Mechanical Manufacturing System Xi'An Jiaotong University China Collaborative Innovation Center of High-End Manufacturing Equipment Xi'An Jiaotong University China

ISBN: (纸本)9781509019472

A novel high- speed image super-resolution algorithm based on sparse representation for MEMS defect detection is proposed in this paper. Traditional super-resolution algorithms adopt a single dictionary to represent images, which cannot differentiate varieties of image blocks and leads to slow processing speed. Aiming at overcoming this shortage of traditional super-resolution algorithms, image blocks are divided into different categories by local features and each of these categories possesses the corresponding high and low resolution dictionary pairs. Experimental results of different MEMS defects show that the improved algorithm can obtain images of little lower quality with much less processing time, indicating that the proposed algorithm is more suitable for MEMS defect detection. © 2016 IEEE.

关键词： image enhancement

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Solving Optimization Problems Through Fully Convolutional Networks: An Application to the Traveling Salesman Problem

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2021年第12期51卷 7475-7485页

作者： Ling, Zhengxuan Tao, xinyu Zhang, Yu Chen, xi Zhejiang Univ Coll Control Sci & Engn Hangzhou 310027 Peoples R China Zhejiang Univ Coll Control Sci & Engn Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

In the new wave of artificial intelligence, deep learning is impacting various industries. As a closely related area, optimization algorithms greatly contribute to the development of deep learning. But the reverse applications are still insufficient. Is there any efficient way to solve certain optimization problem through deep learning? The key is to convert the optimization to a representation suitable for deep learning. In this article, a traveling salesman problem (TSP) is studied. Considering that deep learning is good at image processing, an image representation method is proposed to transfer a TSP to an image. Based on samples of a ten city TSP, a fully convolutional network (FCN) is used to learn the mapping from a feasible region to an optimal solution. The training process is analyzed and interpreted through stages. A visualization method is presented to show how an FCN can understand the training task of a TSP. Once the training is completed, no significant effort is required to solve a new TSP and the prediction is obtained on the scale of milliseconds. The results show good performance in finding the global optimal solution. Moreover, the developed FCN model has been demonstrated on TSP's with different city numbers, proving excellent generalization performance.

关键词： Urban areas Optimization Deep learning Neural networks Feature extraction image resolution Heuristic algorithms Deep learning fully convolutional network (FCN) mapping optimization traveling salesman

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An algorithm to improve the performance of watermarking systems

An algorithm to improve the performance of watermarking syst...

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1st International Congress on image and Signal processing

作者： Duan-quan, Xu Guang-xi, Zhu Huazhong Univ Sci & Technol Elect & Informat Engn Dept Wuhan 430074 Peoples R China

ISBN: (纸本)9780769531199

Random permutation and sharpening are basic methods of image processing. In some digital image watermarking schemes, if original image is preprocessed with random permutation before embedding and the watermarked image is pre-processed with sharpening before detecting, the performance of watermarking systems can be improved, that means the images can seem better, or the veracity rate of detection is higher. In this article, this method is validated in Barni's semi-blind watermarking system. The method is suitable for some semi-blind or blind watermarking systems, whether in spatial domain or in other transform domain.

关键词： Watermarking Discrete cosine transforms Digital images Cryptography Signal processing algorithms image processing Copyright protection Robustness Gaussian distribution image storage

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HDR image multi-bit watermarking using bilateral-filtering-based masking

HDR image multi-bit watermarking using bilateral-filtering-b...

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conference on image processing - algorithms and systems xi

作者： Solachidis, Vassilis Maiorana, Emanuele Campisi, Patrizio Univ Roma Tre Dept Appl Elect I-00146 Rome Italy

ISBN: (纸本)9780819494283

The present paper proposes a blind multi-bit watermarking method for High Dynamic Range (HDR) images. The proposed approach is designed in order to guarantee the watermark imperceptibility both in the HDR marked image and in its Low Dynamic Range (LDR) counterpart, being thus robust against significant non-linear distortions such as those performed by tone-mapping operators (TMOs). In order to do so, the wavelet transform of the Just Noticeable Difference (JND)-scaled space of the original HDR image is employed as embedding domain Moreover, a visual mask taking into account specific aspects of the Human Visual System (HVS) is exploited to improve the quality of the resulting watermarked image. Specifically, bilateral filtering is used to locate information on the detail part of the HDR image, where the watermark should be preferably embedded. A contrast sensitivity function is also employed to modulate the watermark intensity in each wavelet decomposition subband according to its scale and orientation. An extensive set of experimental results testifies the effectiveness of the proposed scheme in embedding multi-bit watermarks into HDR images without affecting the visual quality of the original image, while being robust against TMOs.

关键词： high dynamic range imaging watermarking bilateral filtering tone-mapping operators

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Research on Password algorithms based on Chaos and Algebraic Groups 2

Research on Password Algorithms based on Chaos and Algebraic...

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2nd IEEE International conference on Integrated Circuits and Communication systems, ICICACS 2024

作者： He, xi Northfield Mount Hermon School Gill United States

ISBN: (纸本)9798350317558

As information technology advances rapidly, the demand for data security continues to grow. Chaos theory and algebraic groups have become a focal point in cryptographic research, providing a foundation for designing more robust block cipher algorithms. This article introduces a password algorithm based on chaos and algebraic groups, proposing the Lorenz chaos-geometric Goppa code composite password algorithm that has demonstrated significant achievements in image processing. In comparison to traditional methods, this algorithm exhibits the shortest key length and reduced error rates in image processing, offering a more reliable data protection solution for practical applications. This research opens up new possibilities for enhancing the security and performance in the field of cryptography. © 2024 IEEE.

关键词： Algebraic Group Chaos Cryptography Error Correction Capability Geometric Goppa Code Lorenz Chaos System

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Decomposition of satellite Derived images for the Distinction of Cloud Types' Features

Decomposition of satellite Derived Images for the Distinctio...

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conference on image processing - algorithms and systems xi

作者： Dim, Jules R. Murakami, Hiroshi JAXA EORC Tsukuba Ibaraki 3058505 Japan

ISBN: (纸本)9780819494283

Linear filtering methods using convolution techniques are applied in computer vision, to detect spatial discontinuities in the intensity of luminance of photograph images. These techniques are based on the following principal: a pixel's neighborhood contains information about its intensity. The variation of this intensity provides some information about the distribution and the possible decomposition of the image in various features. This decomposition relies on the relative position of the pixel (edge or not) on the image. These principals, integrated into remote sensing analyses, are applied in this study to differentiate cloud morphological features representing cloud types from a thermal image product (the Cloud top temperatures) derived from polar orbit satellites' observations. This approach contrast with that of other techniques commonly used in satellite cloud classification, and based on optical or thermodynamic properties of the clouds. The interpretation of the distribution of these cloud morphological features, and their frequency is evaluated against another cloud classification method relying on cloud optical properties. The results show a relatively good match between the two classifications. Implications of these results, on the estimation of the impact of cloud shapes' variations on the recent climate are discussed.

关键词： image decomposition pixel intensity local edge gradient cloud top temperature satellite image

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Locally Tuned Inverse Sine Nonlinear Technique for Color image Enhancement

Locally Tuned Inverse Sine Nonlinear Technique for Color Ima...

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conference on image processing - algorithms and systems xi

作者： Arigela, Saibabu Asari, Vijayan K. Univ Dayton Dept Elect & Comp Engn Dayton OH 45419 USA

ISBN: (纸本)9780819494283

In this paper, a novel inverse sine nonlinear transformation based image enhancement technique is proposed to improve the visual quality of images captured in extreme lighting conditions. This method is adaptive, local and simple. The proposed technique consists of four main stages namely histogram adjustment, dynamic range compression, contrast enhancement and nonlinear color restoration. Histogram adjustment on each spectral band is performed to belittle the effect of illumination. Dynamic range compression is accomplished by an inverse sine nonlinear function with a locally tunable image dependent parameter based on the local statistics of each pixel's neighborhood regions of the luminance image. A nonlinear color restoration process based on the chromatic information and luminance of the original image is employed. A statistical quantitative evaluation is performed with the state of the art techniques to analyze and compare the performance of the proposed technique. The proposed technique is also tested on face detection in complex lighting conditions. The results of this technique on images captured in hazy/foggy weather environment are also presented. The evaluation results confirm that the proposed method can be applied to surveillance, security applications in complex lighting environments.

关键词： dynamic range compression inverse sine nonlinearity nonlinear transformation local contrast enhancement nonlinear color restoration face detection

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Non-Reference image Quality Assessment Based on Noise Estimation 25

Non-Reference Image Quality Assessment Based on Noise Estima...

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25th International conference on systems, Signals and image processing (IWSSIP)

作者： Buczkowski, Mateusz Fac Elect & Telecommun Chair Telecommun Syst & Optoelect Poznan Poland

ISBN: (纸本)9781538669792

In this paper a method for estimating mean opinion score for noisy images is presented. The focus is to create a robust method, which is not suited to the single image database. Algorithm is validated using three different databases, which shows that it is more generic than other methods. Moreover, proposed solution works as non-reference image quality assessment algorithm, which means that only distorted image is used for evaluation. Results obtained with proposed method are compared with two other algorithms designed for non-reference IQA.

关键词： Databases image quality Noise measurement Correlation Distortion Estimation Signal processing algorithms"

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A Novel Multi-instance Learning Algorithm with Application to image Classification

A Novel Multi-instance Learning Algorithm with Application t...

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Annual Summit and conference of the Asia-Pacific-Signal-and-Information-processing-Association (APSIPA)

作者： xi, xiaocong Xu, xinshun Wang, xiaolin Shandong Univ Sch Comp Sci & Technol Jinan 250101 Peoples R China

ISBN: (纸本)9781467348638

image classification is an important research topic due to its potential impact on both image processing and understanding. However, due to the inherent ambiguity of image-keyword mapping, this task becomes a challenge. From the perspective of machine learning, image classification task fits the multi-instance learning (MIL) framework very well owing to the fact that a specific keyword is often relevant to an object in an image rather than the entire image. In this paper, we propose a novel MIL algorithm to address image classification task. First, a new instance prototype extraction method is proposed to construct projection space for each keyword. Then, each training sample is mapped to this potential projection space as a point, which converts the MIL problem into standard supervised learning problem. Finally, an SVM is trained for each keyword. The experimental results on a benchmark data set Corel5k demonstrate that the new instance prototype extraction method can result in more reliable instance prototypes and faster running time, and the proposed MIL approach outperforms some state-of-the-art MIL algorithms.

关键词： Learning algorithms

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New image processing challenges for jointly designed electro-optical imaging systems

New image processing challenges for jointly designed electro...

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16th IEEE International conference on image processing

作者： Robinson, M. Dirk Stork, David G. Ricoh Innovat Menlo Pk CA 94025 USA

ISBN: (纸本)9781424456536

Still-image processing algorithms are tailored to and depend crucially upon the properties of the class of images to which they are applied, for instance natural images in consumer digital cameras, medical images in fMRI machines, and binary text images in some photocopiers. We describe a new and possibly very important class of images and tasks for which traditional algorithms seem ill-suited, and for which new algorithms and general methods and concepts are required. This new class of images arises in imaging systems designed through new, joint optimization methods where the optics and the image processing are designed simultaneously in order to yield a high-quality digital image. These new design methods yield intermediate optical images that have unusual spatial, noise and chromatic properties ill-served by traditional image methods. Moreover, these new images present a number of novel challenges in image processing hardware implementations such as constrained space-variance. We describe these briefly new, joint methods for designing digital-optical imaging systems, characterize the intermediate optical images they yield, and some of the digital image processing challenges for producing high-quality still images from these sensed optical images.

关键词： Imaging systems

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