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Automatic image enhancement by learning adaptive patch selection None

Na LI, Jian ZHAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 2,   Pages 206-221 doi: 10.1631/FITEE.1700125

Abstract:

Today, digital cameras are widely used in taking photos. However, some photos lack detail and need enhancement. Many existing image enhancement algorithms are patch based and the patch size is always fixed throughout the image. Users must tune the patch size to obtain the appropriate enhancement. In this study, we propose an automatic image enhancement method based on adaptive patch selection using both dark and bright channels. The double channels enhance images with various exposure problems. The patch size used for channel extraction is selected automatically by thresholding a contrast feature, which is learned systematically from a set of natural images crawled from the web. Our proposed method can automatically enhance foggy or under-exposed/backlit images without any user interaction. Experimental results demonstrate that our method can provide a significant improvement in existing patch-based image enhancement algorithms.

Keywords： Image enhancement     Contrast enhancement     Dark channel     Bright channel     Adaptive patch based processing    

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De-scattering and edge-enhancement algorithms for underwater image restoration Research Papers

Pan-wang PAN, Fei YUAN, En CHENG

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 6,   Pages 862-871 doi: 10.1631/FITEE.1700744

Abstract:

Image restoration is a critical procedure for underwater images, which suffer from serious color deviation and edge blurring. Restoration can be divided into two stages: de-scattering and edge enhancement. First, we introduce a multi-scale iterative framework for underwater image de-scattering, where a convolutional neural network is used to estimate the transmission map and is followed by an adaptive bilateral filter to refine the estimated results. Since there is no available dataset to train the network, a dataset which includes 2000 underwater images is collected to obtain the synthetic data. Second, a strategy based on white balance is proposed to remove color casts of underwater images. Finally, images are converted to a special transform domain for denoising and enhancing the edge using the non-subsampled contourlet transform. Experimental results show that the proposed method significantly outperforms state-of-the-art methods both qualitatively and quantitatively.

Keywords： Image de-scattering     Edge enhancement     Convolutional neural network     Non-subsampled contourlet transform    

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Image Engineering and Its Research Status in China

Zhang Yujin

Strategic Study of CAE 2000, Volume 2, Issue 8,   Pages 91-94

Abstract:

This paper provides a well-regulated explanation of the definition as well as contents of image engineering, a classification of the theories of image engineering and the applications of image technology. In addition, a comprehensive survey on important Chinese publications about image engineering in the past five years is carried out. An analysis and a discussion of the statistics made on the classification results are also presented. This work shows a general and up-to-date picture of the current status, progress trends and application areas of image engineering in China. It also supplies useful information for readers doing research and/or application works in this field, and provides a helpful reference for editors of journals and potential authors of papers.

Keywords： image engineering     publication     survey    

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Grazing incidence polarized light imaging of footwear prints Regular Article

Xin-yi Bi, Rui-fang Han, Ran Liao, Wu-sheng Feng, Da Li, Xue-jie Zhang, Hui Ma,liao.ran@sz.tsinghua.edu.cn

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 11,   Pages 1543-1550 doi: 10.1631/FITEE.1800383

Abstract: Footwear prints are important evidence in criminal investigation. They represent changes in the surface morphology due to disturbance to fine distributions. Existing non-contact optical detection methods usually measure the light intensity contrasts between the footwear prints and the ground, which can be enhanced by grazing incident illumination. We take images of footwear prints on different types of floors using a commercial single lens reflex color camera. Results show that adding linear polarizers in front of the camera lens and light source improves the contrast of footwear print images. The best contrasts are achieved in degree of linear . In addition, the three-color channels of the camera can be used to examine the spectral features of the images. According to the experimental results, the best contrast is obtained at the blue channel. The current work shows that grazing incidence polarized light imaging can effectively enhance the contrast of the footwear prints against the floors, which would help obtain footwear evidence in criminal investigation.

Keywords： 偏振；图像增强；散射；粒子    

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High-payload completely reversible data hiding in encrypted images by an interpolation technique Article

Di XIAO, Ying WANG, Tao XIANG, Sen BAI

Frontiers of Information Technology & Electronic Engineering 2017, Volume 18, Issue 11,   Pages 1732-1743 doi: 10.1631/FITEE.1601067

Abstract: We present a new high-payload joint reversible data-hiding scheme for encrypted images. Instead of embedding data in the encrypted image directly, the content owner first uses an interpolation technique to estimate whether the location can be used for embedding and generates a location map before encryption. Next, the data hider embeds the additional data through flipping the most significant bits (MSBs) of the encrypted image according to the location map. At the receiver side, before extracting the additional data and reconstructing the image, the receiver decrypts the image first. Experimental results demonstrate that the proposed method can achieve real reversibility, which means data extraction and image recovery are free of error. Moreover, our scheme can embed more payloads than most existing reversible data hiding schemes in encrypted images.

Keywords： Encrypted image     Data hiding     Image recovery     Real reversibility     Interpolation    

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Public key based bidirectional shadow image authentication without pixel expansion in image secret sharing Research Article

Xuehu YAN, Longlong LI, Jia CHEN, Lei SUN,publictiger@126.com

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 1,   Pages 88-103 doi: 10.1631/FITEE.2200118

Abstract: (ISS) is gaining popularity due to the importance of digital images and its wide application to cloud-based distributed storage and multiparty secure computing. generally includes shadow image detection and identification, and plays an important role in ISS. However, traditional dealer-participatory methods, which suffer from significant or storing auxiliary information, authenticate the shadow image mainly during the decoding phase, also known as unidirectional authentication. The authentication of the shadow image in the distributing (encoding) phase is also important for the participant. In this study, we introduce a based bidirectional method in ISS without for a (k,n) threshold. When the dealer distributes each shadow image to a corresponding participant, the participant can authenticate the received shadow image with his/her private key. In the decoding phase, the dealer can authenticate each received shadow image with a secret key; in addition, the dealer can losslessly decode the secret image with any k or more shadow images. The proposed method is validated using theoretical analyses, illustrations, and comparisons.

Keywords： Image secret sharing     Shadow image authentication     Public key     Pixel expansion     Lossless decoding    

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Recent Advances in Passive Digital Image Security Forensics: A Brief Review Review

Xiang Lin, Jian-Hua Li, Shi-Lin Wang, Alan-Wee-Chung Liew, Feng Cheng, Xiao-Sa Huang

Engineering 2018, Volume 4, Issue 1,   Pages 29-39 doi: 10.1016/j.eng.2018.02.008

Abstract:

With the development of sophisticated image editing and manipulation tools, the originality and authenticity of a digital image is usually hard to determine visually. In order to detect digital image forgeries, various kinds of digital image forensics techniques have been proposed in the last decade. Compared with active forensics approaches that require embedding additional information, passive forensics approaches are more popular due to their wider application scenario, and have attracted increasing academic and industrial research interests. Generally speaking, passive digital image forensics detects image forgeries based on the fact that there are certain intrinsic patterns in the original image left during image acquisition or storage, or specific patterns in image forgeries left during the image storage or editing. By analyzing the above patterns, the originality of an image can be authenticated. In this paper, a brief review on passive digital image forensic methods is presented in order to provide a comprehensive introduction on recent advances in this rapidly developing research area. These forensics approaches are divided into three categories based on the various kinds of traces they can be used to track—that is, traces left in image acquisition, traces left in image storage, and traces left in image editing. For each category, the forensics scenario, the underlying rationale, and state-of-the-art methodologies are elaborated. Moreover, the major limitations of the current image forensics approaches are discussed in order to point out some possible research directions or focuses in these areas.

Keywords： Digital image forensics     Image-tampering detection     Multimedia security    

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Dual-constraint burst image denoising method Research Articles

Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU,cszhd@zju.edu.cn,cszhl@zju.edu.cn,xdq@zju.edu.cn,ldm@zju.edu.cn

Frontiers of Information Technology & Electronic Engineering 2022, Volume 23, Issue 2,   Pages 220-233 doi: 10.1631/FITEE.2000353

Abstract: has proven to be an effective mechanism for computer vision tasks, especially for and burst . In this paper, we focus on solving the burst problem and aim to generate a single clean image from a burst of noisy images. We propose to combine the power of block matching and 3D filtering (BM3D) and a convolutional neural network (CNN) for burst . In particular, we design a CNN with a divide-and-conquer strategy. First, we employ BM3D to preprocess the noisy burst images. Then, the preprocessed images and noisy images are fed separately into two parallel CNN branches. The two branches produce somewhat different results. Finally, we use a light CNN block to combine the two outputs. In addition, we improve the performance by optimizing the two branches using two different constraints: a signal constraint and a noise constraint. One maps a clean signal, and the other maps the noise distribution. In addition, we adopt block matching in the network to avoid frame misalignment. Experimental results on synthetic and real noisy images show that our algorithm is competitive with other algorithms.

Keywords： Image denoising     Burst image denoising     Deep learning    

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Study of Antistatic and GF Reinforced PA66

Liu Jianqiang

Strategic Study of CAE 2004, Volume 6, Issue 6,   Pages 77-78

Abstract:

Using non-ionic and anionic antistatic agents as composite antistatic system, glass fiber as reinforcing agent, the PA66 was prepared. It has good antistatic and mechanical properties. The influences of composition of the composite antistatic system and glass fiber content on the properties of the antistatic reinforced Nylon-66 were introduced.

Keywords： Nylon-66 resin     antistatic     reinforcing agent    

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A New Algorithm of Fractal Image Coding

Wang Xiuni,Jiang Wei,Wang Licun

Strategic Study of CAE 2006, Volume 8, Issue 1,   Pages 54-57

Abstract:

Because it takes too much of time in fractal image coding, the paper analyses the factors that affect the speed of fractal image coding , and proposes a novel idea by using the reformed variance (tentatively) to improve image fractal compression performance . A theorem is proved that the IFS cannot change the image blocks' reformed variance. Moreover , it gives a novel fractal image compression method based on the reformed variance. The simulation results illuminate that the new method can run fast, at the same time it can improve the PSNR when compared with other fast algorithms.

Keywords： fractal coding     image compression     variance    

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Segmentation and focus-point location based on boundary analysis in forest canopy hemispherical photography Article

Jia-yin SONG,Wen-long SONG,Jian-ping HUANG,Liang-kuan ZHU

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 8,   Pages 741-749 doi: 10.1631/FITEE.1601169

Abstract: Analysis of forest canopy hemisphere images is one of the most important methods for measuring forest canopy structure parameters. In this study, our main focus was on using circular image region segmentation, which is the basis of forest canopy hemispherical photography. The boundary of a forest canopy hemisphere image was analyzed via histogram, rectangle, and Fourier descriptors. The image boundary characteristics were defined and obtained based on the following: (1) an edge model that contains three parts, i.e., step, ramp, and roof; (2) boundary points of discontinuity; (3) an edge that has a linear distribution of scattering points. On this basis, we proposed a segmentation method for the circular region in a forest canopy hemisphere image, fitting the circular boundary and computing the center and radius by the least squares method. The method was unrelated to the parameters of the image acquisition device. Hence, this study lays a foundation for automatically adjusting the parameters of high-performance image acquisition devices used in forest canopy hemispherical photography.

Keywords： Fisheye lens     Least squares method     Image segmentation     Ecology in image processing     Hemispherical photography    

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Image meshing via hierarchical optimization

Hao XIE,Ruo-feng TONG

Frontiers of Information Technology & Electronic Engineering 2016, Volume 17, Issue 1,   Pages 32-40 doi: 10.1631/FITEE.1500171

Abstract:

Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

Keywords： Image meshing     Hierarchical optimization     Convexification    

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Adaptive compression method for underwater images based on perceived quality estimation Regular Papers

Ya-qiong CAI, Hai-xia ZOU, Fei YUAN

Frontiers of Information Technology & Electronic Engineering 2019, Volume 20, Issue 5,   Pages 716-730 doi: 10.1631/FITEE.1700737

Abstract:

Underwater image compression is an important and essential part of an underwater image transmission system. An assessment and prediction method of effectively compressed image quality can assist the system in adjusting its compression ratio during the image compression process, thereby improving the efficiency of the image transmission system. This study first estimates the perceived quality of underwater image compression based on embedded coding compression and compressive sensing, then builds a model based on the mapping between image activity measurement (IAM) and bits per pixel and structural similarity (BPP-SSIM) curves, next obtains model parameters by linear fitting, and finally predicts the perceived quality of the image compression method based on IAM, compression ratio, and compression strategy. Experimental results show that the model can effectively fit the quality curve of underwater image compression. According to the rules of parameters in this model, the perceived quality of underwater compressed images can be estimated within a small error range. The presented method can effectively estimate the perceived quality of underwater compressed images, balance the relationship between the compression ratio and compression quality, reduce the pressure on the data cache, and thus improve the efficiency of the underwater image communication system.

Keywords： Underwater image compression     Set partitioning in hierarchical trees     Compressive sensing     Compression quality estimation    

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Simulating Resin Infusion through Textile Reinforcement Materials for the Manufacture of Complex Composite Structures

Robert S. Pierce, Brian G. Falzon

Engineering 2017, Volume 3, Issue 5,   Pages 596-607 doi: 10.1016/J.ENG.2017.04.006

Abstract:

Increasing demand for weight reduction and greater fuel efficiency continues to spur the use of composite materials in commercial aircraft structures. Subsequently, as composite aerostructures become larger and more complex, traditional autoclave manufacturing methods are becoming prohibitively expensive. This has prompted renewed interest in out-of-autoclave processing techniques in which resins are introduced into a reinforcing preform. However, the success of these resin infusion methods is highly dependent upon operator skill and experience, particularly in the development of new manufacturing strategies for complex parts. Process modeling, as a predictive computational tool, aims to address the issues of reliability and waste that result from traditional trial-and-error approaches. Basic modeling attempts, many of which are still used in industry, generally focus on simulating fluid flow through an isotropic porous reinforcement material. However, recent efforts are beginning to account for the multiscale and multidisciplinary complexity of woven materials, in simulations that can provide greater fidelity. In particular, new multi-physics process models are able to better predict the infusion behavior through textiles by considering the effect of fabric deformation on permeability and porosity properties within the reinforcing material. In addition to reviewing previous research related to process modeling and the current state of the art, this paper highlights the recent validation of a multi-physics process model against the experimental infusion of a complex double dome component. By accounting for deformation-dependent flow behavior, the multi-physics process model was able to predict realistic flow behavior, demonstrating considerable improvement over basic isotropic permeability models.

Keywords： Composite materials     Textile reinforcement     Draping     Infusion     Numerical modeling    

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Amultimodal dense convolution network for blind image quality assessment Research Article

Nandhini CHOCKALINGAM, Brindha MURUGAN

Frontiers of Information Technology & Electronic Engineering 2023, Volume 24, Issue 11,   Pages 1601-1615 doi: 10.1631/FITEE.2200534

Abstract: Technological advancements continue to expand the communications industry’s potential. Images, which are an important component in strengthening communication, are widely available. Therefore, image quality assessment (IQA) is critical in improving content delivered to end users. Convolutional neural networks (CNNs) used in IQA face two common challenges. One issue is that these methods fail to provide the best representation of the image. The other issue is that the models have a large number of parameters, which easily leads to overfitting. To address these issues, the dense convolution network (DSC-Net), a model with fewer parameters, is proposed for . Moreover, it is obvious that the use of multimodal data for has improved the performance of applications. As a result, fuses the texture features extracted using the gray-level co-occurrence matrix (GLCM) method and spatial features extracted using DSC-Net and predicts the image quality. The performance of the proposed framework on the benchmark synthetic datasets LIVE, TID2013, and KADID-10k demonstrates that the MDSC-Net approach achieves good performance over state-of-the-art methods for the NR-IQA task.

Keywords： No-reference image quality assessment (NR-IQA)     Blind image quality assessment     Multimodal dense convolution network (MDSC-Net)     Deep learning     Visual quality     Perceptual quality    

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Title Author Date Type Operation

Automatic image enhancement by learning adaptive patch selection

Na LI, Jian ZHAN

Journal Article

De-scattering and edge-enhancement algorithms for underwater image restoration

Pan-wang PAN, Fei YUAN, En CHENG

Journal Article

Image Engineering and Its Research Status in China

Zhang Yujin

Journal Article

Grazing incidence polarized light imaging of footwear prints

Xin-yi Bi, Rui-fang Han, Ran Liao, Wu-sheng Feng, Da Li, Xue-jie Zhang, Hui Ma,liao.ran@sz.tsinghua.edu.cn

Journal Article

High-payload completely reversible data hiding in encrypted images by an interpolation technique

Di XIAO, Ying WANG, Tao XIANG, Sen BAI

Journal Article

Public key based bidirectional shadow image authentication without pixel expansion in image secret sharing

Xuehu YAN, Longlong LI, Jia CHEN, Lei SUN,publictiger@126.com

Journal Article

Recent Advances in Passive Digital Image Security Forensics: A Brief Review

Xiang Lin, Jian-Hua Li, Shi-Lin Wang, Alan-Wee-Chung Liew, Feng Cheng, Xiao-Sa Huang

Journal Article

Dual-constraint burst image denoising method

Dan ZHANG, Lei ZHAO, Duanqing XU, Dongming LU,cszhd@zju.edu.cn,cszhl@zju.edu.cn,xdq@zju.edu.cn,ldm@zju.edu.cn

Journal Article

Study of Antistatic and GF Reinforced PA66

Liu Jianqiang

Journal Article

A New Algorithm of Fractal Image Coding

Wang Xiuni,Jiang Wei,Wang Licun

Journal Article

Segmentation and focus-point location based on boundary analysis in forest canopy hemispherical photography

Jia-yin SONG,Wen-long SONG,Jian-ping HUANG,Liang-kuan ZHU

Journal Article

Image meshing via hierarchical optimization

Hao XIE,Ruo-feng TONG

Journal Article

Adaptive compression method for underwater images based on perceived quality estimation

Ya-qiong CAI, Hai-xia ZOU, Fei YUAN

Journal Article

Simulating Resin Infusion through Textile Reinforcement Materials for the Manufacture of Complex Composite Structures

Robert S. Pierce, Brian G. Falzon

Journal Article

Amultimodal dense convolution network for blind image quality assessment

Nandhini CHOCKALINGAM, Brindha MURUGAN

Journal Article