Ke Gu

Sichuan Agricultural University

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Guangtao Zhai

Shanghai Jiao Tong University

Xiaokang Yang

Shanghai Jiao Tong University

Weisi Lin

Nanyang Technological University

Wenjun Zhang

Shandong Jiaotong University

Xiongkuo Min

Shanghai Jiao Tong University

Shiqi Wang

City University of Hong Kong

Junfei Qiao

Beijing Academy of Artificial Intelligence

Leida Li

Xidian University

Chengxu Zhou

Beijing University of Technology

Zhifang Xia

Betta Pharmaceuticals (China)

Cooperative Institutions

Shanghai Jiao Tong University

Beijing University of Technology

Beijing Academy of Artificial Intelligence

Chinese Academy of Sciences

Ministry of Education of the People's Republic of China

China University of Mining and Technology

Tsinghua University

Zhejiang University

Harbin Institute of Technology

Nanyang Technological University

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The Analysis of Image Contrast: From Quality Assessment to Automatic Enhancement

IEEE Transactions on Cybernetics (2015)

Ke Gu Guangtao Zhai Weisi Lin Min Liu

Proper contrast change can improve the perceptual quality of most images, but it has largely been overlooked in the current research of image quality assessment (IQA). To fill this void, we in this paper first report a new large dedicated contrast-changed image database (CCID2014), which includes 655 images and associated subjective ratings recorded from 22 inexperienced observers. We then present a novel reduced-reference image quality metric for contrast change (RIQMC) using phase congruency and statistics information of the image histogram. Validation of the proposed model is conducted on contrast related CCID2014, TID2008, CSIQ and TID2013 databases, and results justify the superiority and efficiency of RIQMC over a majority of classical and state-of-the-art IQA methods. Furthermore, we combine aforesaid subjective and objective assessments to derive the RIQMC based Optimal HIstogram Mapping (ROHIM) for automatic contrast enhancement, which is shown to outperform recently developed enhancement technologies.

Contrast Enhancement

Image contrast

10.1109/tcyb.2015.2401732

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Citations (367)

A new no-reference stereoscopic image quality assessment based on ocular dominance theory and degree of parallax

Deleted Journal (2012)

Ke Gu Guangtao Zhai Xiaokang Yang Wenjun Zhang

In recent years, while stereoscopic images were becoming widely applied and the corresponding technologies were substantially developed, very few stereoscopic image quality assessment metrics were proposed, especially under the condition that there is no reference image available. This paper proposes a new no-reference stereoscopic image quality assessment algorithm based on the ocular dominance theory and degree of parallax. All of our tests using the Toyama database draw two valuable conclusions. First, the performances of stereoscopic image quality assessment methods are significantly affected by difference of image qualities between the left and right images. Second, to offset the discriminations of different degrees of parallax caused by various 3D image contents is required indeed. Experiments and comparative studies are provided to confirm the effectiveness of our proposed new stereoscopic image quality metric.

Parallax

Stereo image

Quality Assessment

Dominance (genetics)

Source

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Citations (9)

No-Reference Stereoscopic IQA Approach: From Nonlinear Effect to Parallax Compensation

Journal of Electrical and Computer Engineering (2012)

Ke Gu Guangtao Zhai Xiaokang Yang Wenjun Zhang

The last decade has seen a booming of the applications of stereoscopic images/videos and the corresponding technologies, such as 3D modeling, reconstruction, and disparity estimation. However, only a very limited number of stereoscopic image quality assessment metrics was proposed through the years. In this paper, we propose a new no‐reference stereoscopic image quality assessment algorithm based on the nonlinear additive model, ocular dominance model, and saliency based parallax compensation. Our studies using the Toyama database result in three valuable findings. First, quality of the stereoscopic image has a nonlinear relationship with a direct summation of two monoscopic image qualities. Second, it is a rational assumption that the right‐eye response has the higher impact on the stereoscopic image quality, which is based on a sampling survey in the ocular dominance research. Third, the saliency based parallax compensation, resulted from different stereoscopic image contents, is considerably valid to improve the prediction performance of image quality metrics. Experimental results confirm that our proposed stereoscopic image quality assessment paradigm has superior prediction accuracy as compared to state‐of‐the‐art competitors.

Parallax

Stereo image

10.1155/2012/436031

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Citations (31)

Remote Sensing Inversion for River Turbidity Estimation Based on Noise Injection and Ensemble Learning

2021 China Automation Congress (CAC) (2021)

Yazhen Zhou Shuang Shi Hongyan Liu Yonghui Zhang Ke Gu

Turbidity is one main indicator of quality typically used for monitoring water pollution and water eutrophication. The monitoring technology of river water quality can be divided into contact monitoring technology based on physical sensors and non-contact monitoring technology based on remote sensing satellites. The traditional contact monitoring technology has limited monitoring spatial resolution. As a new technology, the remote sensing monitoring technology of the river can conveniently solve the above-mentioned shortcomings and realize the coverage of the whole river basin. However, due to the scarcity of remote sensing data and the measurement errors of remote sensing satellites themselves, learning-based turbidity measurement methods have weak generalization ability and thus are easily prone to produce large deviations. To effectively address such difficulty, a noise injection method is introduced into a river turbidity measurement model based on ensemble learning. To be specific, we randomly generate many noise sets and add them to the remote sensing data to make it closer to the real application scene, and then adopt the ensemble learning model to estimate river turbidity. Experiments validate that the random noise injection strategy can effectively improve the generalization performance of river turbidity measurement.

Turbidity

10.1109/cac53003.2021.9727612

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Citations (1)

Convolutional Neural Network for Smoke Image Super-Resolution

Proceedings of the 2nd International Conference on Computer Science and Application Engineering (2018)

Maoshen Liu Ke Gu Junfei Qiao

The1 flare stack is the last line of defense in the safe production of large-scale chemical plants. Monitoring black smoke produced by the incomplete flare stack exhaust combustion can effectively reduce environmental pollution and production accident. In order to improve the ability to recognition and analyze the black smoke, high-resolution flare stack scene images are in urgent need. To this end, we in this paper propose a super-resolution algorithm based on convolutional network that focuses only on smoke area for the purpose of identifying the smoke of flare stack. With a lightweight convolutional neural network structure, our network specializes in learning smoke characteristics mapping between the low-resolution images and the associated high-resolution. To verify validity, our algorithm compares the super-resolution quality of the smoky region of the flare stack image with several classic super-resolution algorithms. The experimental results show that our algorithm is superior to the classical algorithms when applied to smoke images.

Flare

10.1145/3207677.3277950

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Citations (5)

Blind Quality Metric of DIBR-Synthesized Images in the Discrete Wavelet Transform Domain

IEEE Transactions on Image Processing (2019)

Guangcheng Wang Zhongyuan Wang Ke Gu Leida Li Zhifang Xia

Free viewpoint video (FVV) has received considerable attention owing to its widespread applications in several areas such as immersive entertainment, remote surveillance and distanced education. Since FVV images are synthesized via a depth image-based rendering (DIBR) procedure in the "blind" environment (without reference images), a real-time and reliable blind quality assessment metric is urgently required. However, the existing image quality assessment metrics are insensitive to the geometric distortions engendered by DIBR. In this research, a novel blind method of DIBR-synthesized images is proposed based on measuring geometric distortion, global sharpness and image complexity. First, a DIBR-synthesized image is decomposed into wavelet subbands by using discrete wavelet transform. Then, the Canny operator is employed to detect the edges of the binarized low-frequency subband and high-frequency subbands. The edge similarities between the binarized low-frequency subband and high-frequency subbands are further computed to quantify geometric distortions in DIBR-synthesized images. Second, the log-energies of wavelet subbands are calculated to evaluate global sharpness in DIBR-synthesized images. Third, a hybrid filter combining the autoregressive and bilateral filters is adopted to compute image complexity. Finally, the overall quality score is derived to normalize geometric distortion and global sharpness by the image complexity. Experiments show that our proposed quality method is superior to the competing reference-free state-of-the-art DIBR-synthesized image quality models.

10.1109/tip.2019.2945675

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Citations (69)

A Cross-Font Image Retrieval Network for Recognizing Undeciphered Oracle Bone Inscriptions

arXiv (Cornell University) (2024)

Zhicong Wu Qi Su Ke Gu Xiaodong Shi

Oracle Bone Inscription (OBI) is the earliest mature writing system known in China to date, which represents a crucial stage in the development of hieroglyphs. Nevertheless, the substantial quantity of undeciphered OBI characters continues to pose a persistent challenge for scholars, while conventional methods of ancient script research are both time-consuming and labor-intensive. In this paper, we propose a cross-font image retrieval network (CFIRN) to decipher OBI characters by establishing associations between OBI characters and other script forms, simulating the interpretive behavior of paleography scholars. Concretely, our network employs a siamese framework to extract deep features from character images of various fonts, fully exploring structure clues with different resolution by designed multiscale feature integration (MFI) module and multiscale refinement classifier (MRC). Extensive experiments on three challenging cross-font image retrieval datasets demonstrate that, given undeciphered OBI characters, our CFIRN can effectively achieve accurate matches with characters from other gallery fonts.

Font

10.48550/arxiv.2409.06381

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Citations (0)

Exploiting neural models for no-reference image quality assessment

Cenhui Pan Yi Xu Yichao Yan Ke Gu Xiaokang Yang

We propose an improved algorithm for no-reference image quality assessment (NR-IQA) using the convolutional neural network (CNN) and neural theory based saliency detection. Firstly, we extract non-overlapping patches from the input image. For each patch, we obtain the quality score by CNN network, which consists of seven layers and integrates feature learning and regression into image patch quality estimation. Considering that the patches attracting much attention take significant role in visual perception, an efficient technique based on free energy based neural model is used to detect the saliency map. This saliency map is then applied as a weighting mask to output the quality score of the whole image. Results of experiments show that our algorithm achieves state-of-the-art performance, as compared with the prevailing IQA methods.

Feature (linguistics)

Quality Score

10.1109/vcip.2016.7805524

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Citations (20)

Blind quality assessment of compressed images via pseudo structural similarity

2022 IEEE International Conference on Multimedia and Expo (ICME) (2016)

Xiongkuo Min Guangtao Zhai Ke Gu Yuming Fang Xiaokang Yang

Block-based compression causes severe pseudo structures. We find that the pseudo structures of images compressed by different levels show some degree of similarity. So we propose to evaluate the quality of compressed images via the similarity between pseudo structures of two images. To obtain a "reference" image, we introduce the most distorted image (MDI), which is derived from the distorted image and suffers from the highest degree of compression. The proposed pseudo structural similarity (PSS) model calculates the similarity between pseudo structures of the distorted image and MDI. Pseudo structures of the distorted image become similar to the MDI's under the condition of severe compression. Via comparative tests, the proposed PSS model, on one hand, is shown to be comparable to state-of-the-art competitors, and on the other hand, it is not only good at assessing natural scene images but also performs the best in the hotly-researched screen content image (SCI) database. It deserves to mention that PSS is able to boost the performance of mainstream general-purpose no-reference (NR) quality measures.

Similarity (geometry)

Distortion (music)

10.1109/icme.2016.7552955

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Citations (40)

Using Structural degradation and Parallax for reduced-reference quality assessment of 3D images

Qi Xu Guangtao Zhai Min Liu Ke Gu

Owing to the thriving market of stereoscopic image based applications, efficient and effective 3D image quality assessment (IQA) techniques become colossally required these days. Consequently, we introduce a new reduced-reference (RR) stereoscopic image quality metric to meet this demand, through measuring Structural degradation and Saliency based Parallax compensation Model (SSPM). Experimental results on the LIVE 3D Image Quality Database, including both symmetrically and asymmetrically distorted stereoscopic images in different categories and quality levels, are provided to justify the effectiveness of the proposed SSPM model as compared to some existing progressive and popular stereoscopic IQA approaches. Meanwhile, it deserves broad attentions that only four number pairs, extracted from original image, are required as the key feature to be sent to the receiver terminal, thus making this procedure also efficient.

Parallax

Degradation

Quality Assessment

10.1109/bmsb.2014.6873484

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Citations (16)