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2018 IEEE International Conference on Computational Photography (ICCP)最新文献

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Automatic estimation of modulation transfer functions 调制传递函数的自动估计
2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-01 DOI: 10.1109/ICCPHOT.2018.8368467
M. Bauer, V. Volchkov, M. Hirsch, B. Scholkopf
{"title":"Automatic estimation of modulation transfer functions","authors":"M. Bauer, V. Volchkov, M. Hirsch, B. Scholkopf","doi":"10.1109/ICCPHOT.2018.8368467","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368467","url":null,"abstract":"The modulation transfer function (MTF) is widely used to characterise the performance of optical systems. Measuring it is costly and it is thus rarely available for a given lens specimen. Fortunately, images recorded through an optical system contain ample information about its MTF, only that it is confounded with the statistics of the images. This work presents a method to estimate the MTF of camera lens systems directly from photographs, without the need for expensive equipment. We use a custom grid display to accurately measure the point response of lenses to acquire ground truth training data. We then use the same lenses to record natural images and employ a supervised learning approach using a convolutional neural network to estimate the MTF on small image patches, aggregating the information into MTF charts over the entire field of view. It generalises to unseen lenses and can be applied for single photographs, with the performance improving if multiple photographs are available.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"137 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121402397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 8
ADP: Automatic differentiation ptychography ADP:自动分化型图
2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-05-01 DOI: 10.1109/ICCPHOT.2018.8368470
Sushobhan Ghosh, Y. Nashed, O. Cossairt, A. Katsaggelos
{"title":"ADP: Automatic differentiation ptychography","authors":"Sushobhan Ghosh, Y. Nashed, O. Cossairt, A. Katsaggelos","doi":"10.1109/ICCPHOT.2018.8368470","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368470","url":null,"abstract":"Ptychography is an imaging technique which aims to recover the complex-valued exit wavefront of an object from a set of its diffraction pattern magnitudes. Ptychography is one of the most popular techniques for sub-30 nanometer imaging as it does not suffer from the limitations of typical lens based imaging techniques. The object can be reconstructed from the captured diffraction patterns using iterative phase retrieval algorithms. Over time many algorithms have been proposed for iterative reconstruction of the object based on manually derived update rules. In this paper, we adapt automatic differentiation framework to solve practical and complex ptychographic phase retrieval problems and demonstrate its advantages in terms of speed, accuracy, adaptability and generalizability across different scanning techniques.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117072810","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 14
SH-ToF: Micro resolution time-of-flight imaging with superheterodyne interferometry SH-ToF:超外差干涉的微分辨率飞行时间成像
2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-04-10 DOI: 10.1109/ICCPHOT.2018.8368473
Fengqiang Li, F. Willomitzer, Prasanna V. Rangarajan, Mohit Gupta, A. Velten, O. Cossairt
{"title":"SH-ToF: Micro resolution time-of-flight imaging with superheterodyne interferometry","authors":"Fengqiang Li, F. Willomitzer, Prasanna V. Rangarajan, Mohit Gupta, A. Velten, O. Cossairt","doi":"10.1109/ICCPHOT.2018.8368473","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368473","url":null,"abstract":"Three dimensional imaging techniques have been widely used in both industry and academia. Time-of-flight (ToF) sensors offer a promising method of 3D imaging due to compact size and low complexity. However, state-of-the-art ToF sensors only have depth resolutions of centimeters due to limitations in the modulation frequencies that can be used. In this paper, we propose a technique to generate modulation frequencies as high as 1 THz using optical superheterodyne interferometry. Our proposed system provides great flexibility in imaging range and resolution. We experimentally demonstrate an increase in depth resolution by an order of magnitude relative to currently available commercial ToF cameras.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133921811","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 19
Fast and accurate reconstruction of compressed color light field 压缩色光场的快速准确重建
2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-01-31 DOI: 10.1109/ICCPHOT.2018.8368477
Ofir Nabati, D. Mendlovic, R. Giryes
{"title":"Fast and accurate reconstruction of compressed color light field","authors":"Ofir Nabati, D. Mendlovic, R. Giryes","doi":"10.1109/ICCPHOT.2018.8368477","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368477","url":null,"abstract":"Light field photography has been studied thoroughly in recent years. One of its drawbacks is the need for multi-lens in the imaging. To compensate that, compressed light field photography has been proposed to tackle the trade-offs between the spatial and angular resolutions. It obtains by only one lens, a compressed version of the regular multi-lens system. The acquisition system consists of a dedicated hardware followed by a decompression algorithm, which usually suffers from high computational time. In this work, we propose a computationally efficient neural network that recovers a high-quality color light field from a single coded image. Unlike previous works, we compress the color channels as well, removing the need for a CFA in the imaging system. Our approach outperforms existing solutions in terms of recovery quality and computational complexity. We propose also a neural network for depth map extraction based on the decompressed light field, which is trained in an unsupervised manner without the ground truth depth map.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128896677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 21
Reblur2Deblur: Deblurring videos via self-supervised learning Reblur2Deblur:通过自我监督学习去模糊视频
2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2018-01-16 DOI: 10.1109/ICCPHOT.2018.8368468
Huaijin Chen, Jinwei Gu, Orazio Gallo, Ming-Yu Liu, A. Veeraraghavan, J. Kautz
{"title":"Reblur2Deblur: Deblurring videos via self-supervised learning","authors":"Huaijin Chen, Jinwei Gu, Orazio Gallo, Ming-Yu Liu, A. Veeraraghavan, J. Kautz","doi":"10.1109/ICCPHOT.2018.8368468","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368468","url":null,"abstract":"Motion blur is a fundamental problem in computer vision as it impacts image quality and hinders inference. Traditional deblurring algorithms leverage the physics of the image formation model and use hand-crafted priors: they usually produce results that better reflect the underlying scene, but present artifacts. Recent learning-based methods implicitly extract the distribution of natural images directly from the data and use it to synthesize plausible images. Their results are impressive, but they are not always faithful to the content of the latent image. We present an approach that bridges the two. Our method fine-tunes existing deblurring neural networks in a self-supervised fashion by enforcing that the output, when blurred based on the optical flow between subsequent frames, matches the input blurry image. We show that our method significantly improves the performance of existing methods on several datasets both visually and in terms of image quality metrics.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133729022","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 73
Learned perceptual image enhancement 习得感知图像增强
2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2017-12-07 DOI: 10.1109/ICCPHOT.2018.8368474
Hossein Talebi Esfandarani, P. Milanfar
{"title":"Learned perceptual image enhancement","authors":"Hossein Talebi Esfandarani, P. Milanfar","doi":"10.1109/ICCPHOT.2018.8368474","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368474","url":null,"abstract":"Learning a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images. While L1 and L2 losses are perhaps the most widely used functions for this purpose, they do not necessarily lead to perceptually compelling results. In this paper, we show that adding a learned no-reference image quality metric to the loss can significantly improve enhancement operators. This metric is implemented using a CNN (convolutional neural network) trained on a large-scale dataset labelled with aesthetic preferences ofhuman raters. This loss allows us to conveniently perform back-propagation in our learning framework to simultaneously optimize for similarity to a given ground truth reference and perceptual quality. This perceptual loss is only used to train parameters of image processing operators, and does not impose any extra complexity at inference time. Our experiments demonstrate that this loss can be effective for tuning a variety of operators such as local tone mapping and dehazing.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-12-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125212735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 69
BLADE: Filter learning for general purpose computational photography BLADE:用于通用计算摄影的过滤器学习
2018 IEEE International Conference on Computational Photography (ICCP) Pub Date : 2017-11-29 DOI: 10.1109/ICCPHOT.2018.8368476
Pascal Getreuer, I. Garcia-Dorado, J. Isidoro, Sungjoon Choi, Frank Ong, P. Milanfar
{"title":"BLADE: Filter learning for general purpose computational photography","authors":"Pascal Getreuer, I. Garcia-Dorado, J. Isidoro, Sungjoon Choi, Frank Ong, P. Milanfar","doi":"10.1109/ICCPHOT.2018.8368476","DOIUrl":"https://doi.org/10.1109/ICCPHOT.2018.8368476","url":null,"abstract":"The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicking, and stylization.","PeriodicalId":413914,"journal":{"name":"2018 IEEE International Conference on Computational Photography (ICCP)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130032583","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 26
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