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example-based Image Colorization Using Locality Consistent Sparse Representation

IEEE TRANSACTIONS ON IMAGE PROCESSING 2017年第11期26卷 5188-5202页

作者： Li, Bo Zhao, Fuchen Su, Zhuo Liang, Xiangguo Lai, Yu-Kun Rosin, Paul L. Nanchang Hangkong Univ Sch Math & Informat Sci Nanchang 330063 Jiangxi Peoples R China Sun Yat Sen Univ Natl Engn Res Ctr Sch Data & Comp Sci Guangzhou 510006 Guangdong Peoples R China Cardiff Univ Sch Comp Sci & Informat Cardiff CF24 3AA S Glam Wales

Image colorization aims to produce a natural looking color image from a given gray-scale image, which remains a challenging problem. In this paper, we propose a novel example-based image colorization method exploiting a new locality consistent sparse representation. Given a single reference color image, our method automatically colorizes the target gray-scale image by sparse pursuit. For efficiency and robustness, our method operates at the superpixel level. We extract low-level intensity features, mid-level texture features, and high-level semantic features for each superpixel, which are then concatenated to form its descriptor. The collection of feature vectors for all the superpixels from the reference image composes the dictionary. We formulate colorization of target superpixels as a dictionary-based sparse reconstruction problem. Inspired by the observation that superpixels with similar spatial location and/or feature representation are likely to match spatially close regions from the reference image, we further introduce a locality promoting regularization term into the energy formulation, which substantially improves the matching consistency and subsequent colorization results. Target superpixels are colorized based on the chrominance information from the dominant reference superpixels. Finally, to further improve coherence while preserving sharpness, we develop a new edge-preserving filter for chrominance channels with the guidance from the target gray-scale image. To the best of our knowledge, this is the first work on sparse pursuit image colorization from single reference images. Experimental results demonstrate that our colorization method outperforms the stateof-the-art methods, both visually and quantitatively using a user study.

关键词： Image colorization example-based dictionary sparse representation locality edge-preserving

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example-based Regularization Deployed to Super-Resolution Reconstruction of a Single Image

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COMPUTER JOURNAL 2009年第1期52卷 15-30页

作者： Elad, Michael Datsenko, Dmitry Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

In super-resolution (SR) reconstruction of images, regularization becomes crucial when insufficient number of measured low-resolution images is supplied. Beyond making the problem algebraically well posed, a properly chosen regularization can direct the solution toward a better quality outcome. Even the extreme case-025EFa SR reconstruction from a single measured image-025EFcan be made successful with a well-chosen regularization. Much of the progress made in the past two decades on inverse problems in image processing can be attributed to the advances in forming or choosing the way to practice the regularization. A Bayesian point of view interpret this as a way of including the prior distribution of images, which sheds some light on the complications involved. This paper reviews an emerging powerful family of regularization techniques that is drawing attention in recent years-025EFthe example-based approach. We describe how examples can and have been used effectively for regularization of inverse problems, reviewing the main contributions along these lines in the literature, and organizing this information into major trends and directions. A description of the state-of-the-art in this field, along with supporting simulation results on the image scale-up problem are given. This paper concludes with an outline of the outstanding challenges this field faces today.

关键词： Regularization example-based nearest neighbor Bayesian reconstruction MMSE MAP PCA clustering K-D tree scale-up super-resolution

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example-based Super-Resolution Land Cover Mapping Using Support Vector Regression

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2014年第4期7卷 1271-1283页

作者： Zhang, Yihang Du, Yun Ling, Feng Fang, Shiming Li, Xiaodong Chinese Acad Sci Inst Geodesy & Geophys Key Lab Monitoring & Estimate Environm & Disaster Wuhan 430077 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China China Univ Geosci Sch Publ Adm Wuhan 430074 Peoples R China

Super-resolution mapping (SRM) is a promising technique to generate a fine resolution land cover map from coarse fractional images by predicting the spatial locations of different land cover classes at subpixel scale. In most cases, SRM is accomplished by using the spatial dependence principle, which is a simple method to describe the spatial patterns of different land cover classes. However, the spatial dependence principle used in existing SRM models does not fully reflect the real-world situations, making the resultant fine resolution land cover map often have uncertainty. In this paper, an example-based SRM model using support vector regression (SVR_SRM) was proposed. Without directly using an explicit formulation to describe the prior information about the subpixel spatial pattern, SVR_SRM generates a fine resolution land cover map from coarse fractional images, by learning the nonlinear relationships between the coarse fractional pixels and corresponding labeled subpixels from the selected best-match training data. based on the experiments of two subset images of National Land Cover Database (NLCD) 2001 and a subset of real hyperspectral Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) image, the performance of SVR_SRM was evaluated by comparing with the traditional pixel-based hard classification (HC) and several existing typical SRM algorithms. The results show that SVR_SRM can generate fine resolution land cover maps with more detailed spatial information and higher accuracy at different spatial scales.

关键词： example-based K-D tree machine learning super-resolution land cover mapping support vector regression (SVR)

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example-based Face Image Super-Resolution Taking into Consideration Correspondence of Facial Parts

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IEEJ TRANSACTIONS ON ELECTRICAL AND ELECTRONIC ENGINEERING 2017年第6期12卷 917-924页

作者： Hamdan, Suhail Fukumizu, Yohei Izumi, Tomonori Yamauchi, Hironori Ritsumeikan Univ Grad Sch Sci & Engn 1-1-1 Noji Higashi Kusatsu Shiga 5258577 Japan

example-based methods are widely adopted in image super-resolution (SR) to generate clear, high-resolution (HR) images from low-resolution (LR) images. This paper reports our study of example-based algorithms on LR facial images that exploit the relationship between LR and HR image patch pairs in a database using a Markov random field (MRF) model. We aimed to restore each part in a face with patches related to the parts. We generated patches with information on their original positions from a set of normalized facial images where their facial feature points were approximately in the same position. The nearer candidate patch's position to the target position yielded higher compatibility of the facial parts. Our algorithm restored LR images by combining the proposed facial parts' compatibility function with the conventional function to find the best set of estimated HR patches. The final SR results were obtained by stitching the inferred HR patches from an iteration process. An experiment on a set of facial images demonstrates that the combined compatibility function achieves the best quality in the resulting image, i.e. 30.39 [dB], in terms of the peak signal-to-noise ratio (PSNR) compared to the previously achieved quality of 29.65 [dB]. (C) 2017 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

关键词： super-resolution image restoration example-based patch Markov random field facial parts

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example-based Super-Resolution via Structure Analysis of Patches

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IEEE SIGNAL PROCESSING LETTERS 2013年第4期20卷 407-410页

作者： Kim, Changhyun Choi, Kyuha Ra, Jong Beom Korea Adv Inst Sci & Technol Dept Elect Engn Taejon 305701 South Korea

In example-based super-resolution, it is difficult to determine appropriate high-frequency (HF) patches from a training database by using only the information of one input image. In this letter, we utilize the sharpness of high-resolution (HR) patch candidates for the reliable determination of HF patches. For each input patch, we first preselect a sufficient number of HF patch candidates and produce HR patches by adding the candidates to the input patch. After removing the outlier patches, we then reselect several HF patches according to the patch characteristic for producing the final HR image. This reselection procedure is optimized for edge patches and non-edge patches, respectively. Experimental results show that the proposed algorithm provides sharper details compared to the existing algorithms.

关键词： example-based reconstruction constraint sharpness super-resolution

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example-based terrain synthesis with pit removal

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COMPUTERS & GRAPHICS-UK 2021年 99卷 43-53页

作者： Scott, Joshua J. Dodgson, Neil A. Victoria Univ Wellington Wellington New Zealand

Artificial terrain synthesis is challenging because natural terrain contains many types of features generated by a diverse range of natural processes over vastly different time-scales. example-based terrain synthesis has been used to overcome the challenge by using real-world terrain as the basis for synthesis. This produces good local behaviour but can have poor global behaviour. We introduce fluid-flow solutions that can be overlaid on multi-resolution example-based terrain synthesis to improve global realism. We apply these to an existing pixel-based terrain synthesis method and to a novel terrain synthesis method, terrain optimisation , based on texture optimisation. Our fluid-flow solutions improve results for artificial terrains. (c) 2021 Elsevier Ltd. All rights reserved.

关键词： example-based Optimization Terrain

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example-based Enhancement of Degraded Video

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IEEE SIGNAL PROCESSING LETTERS 2014年第9期21卷 1140-1144页

作者： Hung, Edson M. Garcia, Diogo C. de Queiroz, Ricardo L. Univ Brasilia Gama Engn Sch BR-70910900 Brasilia DF Brazil

We present an example-based approach to general enhancement of degraded video frames. The method relies on building a dictionary with non-degraded parts of the video and to use such a dictionary to enhance the degraded parts. The image degradation has to originate from a "repeatable" process, so that the dictionary image patches (blocks) are equally degraded, thus originating a dictionary with degraded blocks and their residues (differences in between degraded and original blocks). Once a match is found between a degraded block in the video and a degraded block in the dictionary, the associated residue of the latter is soft-added to the block of the former. The method is a generalization of the method for example-based super-resolution. Results are presented to demonstrate the applicability of the method to many scenarios.

关键词： example-based noise removal super-resolution video enhancement

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example-based single document image super-resolution: a global MAP approach with outlier rejection

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MULTIDIMENSIONAL SYSTEMS AND SIGNAL PROCESSING 2007年第2-3期18卷 103-121页

作者： Datsenko, Dmitry Elad, Michael Technion Israel Inst Technol Dept Comp Sci IL-32000 Haifa Israel

Regularization plays a vital role in inverse problems, and especially in ill-posed ones. Along with classical regularization techniques based on smoothness, entropy, and sparsity, an emerging powerful regularization is one that leans on image examples. In this paper, we propose an efficient scheme for using image examples as driving a powerful regularization, applied to the image scale-up (super-resolution) problem. In this work, we target specifically scanned documents containing written text, graphics, and equations. Our algorithm starts by assigning per each location in the degraded image several candidate high-quality patches. Those are found as the nearest-neighbors (NN) in an image-database that contains pairs of corresponding low- and high-quality image patches. The found examples are used for the definition of an image prior expression, merged into a global MAP penalty function. We use this penalty function both for rejecting some of the irrelevant outlier examples, and then for reconstructing the desired image. We demonstrate our algorithm on several scanned documents with promising results.

关键词： regularization example-based nearest-neighbor Bayesian reconstruction MMSE MAP outliers K-D tree

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example-based Color Stylization of Images

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ACM Transactions on Applied Perception 2005年第3期2卷 322-345页

作者： Chang, Youngha Saito, Suguru Uchikawa, Keiji Nakajima, Masayuki Tokyo Institute of Technology Japan

We describe a new computational approach to stylize the colors of an image by using a reference image. During processing, we take the characteristics of human color perception into account to generate more appealing results. Our system starts by classifying each pixel value into one of the basic color categories, derived from our psychophysical experiments. The basic color categories are perceptual categories that are universal to everyone, regardless of nationality or cultural background. These categories are used to provide restrictions on color transformations to avoid generating unnatural results. Our system then renders a new image by transferring colors from a reference image to the input image, based on these categorizations. To avoid artifacts due to the explicit clustering, our system defines fuzzy categorization when pseudocontours appear in the resulting image. We present a variety of results and show that our method performs a large, yet natural, color transformation without any sense of incongruity and that the resulting images automatically capture the characteristics of the colors used in the reference image. © 2005, ACM. All rights reserved.

关键词： Algorithms Basic color categories color transfer Design example-based Experimentation

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example-based Automatic Caricature Generation

Example-Based Automatic Caricature Generation

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International Conference on Cyberworlds (CW)

作者： Yang, Wei Tajima, Kouki Xu, Jiayi Toyoura, Masahiro Mao, Xiaoyang Univ Yamanashi Kofu Yamanashi Japan Hangzhou Dianzi Univ Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781479946778

Caricature is a popular artistic media widely used for effective communications. The fascination of caricature lies in its expressive depiction of a person's prominent features, which is usually realized through the so called exaggeration technique. This paper proposes a new example based automatic caricature generation system supporting the exaggeration of visual appearance features. The system comprises the construction of a learning database and the generation of caricatures. The construction of the learning database links the pairs of facial images and corresponding caricatures. Given an input face, the system automatically compute the feature vectors of facial parts and hairstyle, and search the learning database for the exaggerated parts by using the most prominent features. Experimental results show that our system can achieve the control over the degree of exaggeration and the exaggerated results can better represent the features of the subjects.

关键词： caricature example-based exaggeration visual appearance facial feature

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