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We consider how image super resolution (SR) can contribute to an object detection task in low-resolution images.
Image quality assessment: from error visibility to structural similarity
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: · 2004
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A training-based no-reference image quality assessment algorithm
Luo, H.: · 2004
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Image super-resolution survey
van Ouwerkerk, J.D.: · 2006
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Simultaneous super-resolution and feature extraction for recognition of low-resolution faces
Hennings-Yeomans, P.H., Baker, S., Kumar, B.V.: · 2008
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Robust low-resolution face identification and verification using high-resolution features
Hennings-Yeomans, P.H., Kumar, B.V., Baker, S.: · 2009
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Exploiting self-similarities for single frame super-resolution
Yang, C.Y., Huang, J.B., Yang, M.H.: · 2010
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Single-image super-resolution using sparse regression and natural image prior
Kim, K.I., Kwon, Y.: · 2010
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Facial deblur inference using subspace analysis for recognition of blurred faces
Nishiyama, M., Hadid, A., Takeshima, H., Shotton, J., Kozakaya, T., Yamaguchi, O.: · 2011
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An effective document image deblurring algorithm
Chen, X., He, X., Yang, J., Wu, Q.: · 2011
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Synthesis-based recognition of low resolution faces
Shekhar, S., Patel, V.M., Chellappa, R.: · 2011
Earlier work this paper cites.
Sparse representation-based super resolution for face recognition at a distance
Bilgazyev, E., Efraty, B.A., Shah, S.K., Kakadiaris, I.A.: · 2011
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Adaptive denoising filtering for object detection applications
Milani, S., Bernardini, R., Rinaldo, R.: · 2012
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Objective quality assessment for image super-resolution: A natural scene statistics approach
Yeganeh, H., Rostami, M., Wang, Z.: · 2012
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Coupled dictionary training for image super-resolution
Yang, J., Wang, Z., Lin, Z., Cohen, S., Huang, T.: · 2012
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Benchmarking of quality metrics on ultra-high definition video sequences
Hanhart, P., Korshunov, P., Ebrahimi, T.: · 2013
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Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., Fergus, R.: · 2013
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Nonparametric blind super-resolution
Michaeli, T., Irani, M.: · 2013
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Landmark image super-resolution by retrieving web images
Yue, H., Sun, X., Yang, J., Wu, F.: · 2013
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Fast image super-resolution based on in-place example regression
Yang, J., Lin, Z., Cohen, S.: · 2013
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Selective search for object recognition
Uijlings, J.R.R., van de Sande, K.E.A., Gevers, T., Smeulders, A.W.M.: · 2013
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Blind image quality assessment using semi-supervised rectifier networks
Tang, H., Joshi, N., Kapoor, A.: · 2014
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Convolutional neural networks for no-reference image quality assessment
Kang, L., Ye, P., Li, Y., Doermann, D.: · 2014
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Image quality assessment for fake biometric detection: Application to iris, fingerprint, and face recognition
Galbally, J., Marcel, S., Fiérrez, J.: · 2014
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A method for the evaluation of image quality according to the recognition effectiveness of objects in the optical remote sensing image using machine learning algorithm
Yuan, T., Zheng, X., Hu, X., Zhou, W., Wang, W.: · 2014
Cited alongside, same era.
Super-resolution: a comprehensive survey
Nasrollahi, K., Moeslund, T.B.: · 2014
Cited alongside, same era.
Single-image super-resolution: A benchmark
Yang, C.Y., Ma, C., Yang, M.H.: · 2014
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Cited alongside, same era.
Edge boxes: Locating object proposals from edges
Zitnick, C.L., Dollár, P.: · 2014
Cited alongside, same era.
Full-reference visual quality assessment for synthetic images: A subjective study
Kundu, D., Evans, B.L.: · 2015
Accurate image super-resolution using very deep convolutional networks
Kim, J., Kwon Lee, J., Mu Lee, K.: · 2016
Later among the works it cites.
Deeply-recursive convolutional network for image super-resolution
Kim, J., Kwon Lee, J., Mu Lee, K.: · 2016
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Accelerating the super-resolution convolutional neural network
Dong, C., Loy, C.C., Tang, X.: · 2016
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Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: · 2016
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Ultra-resolving face images by discriminative generative networks
Yu, X., Porikli, F.: · 2016
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What makes for effective detection proposals?
Hosang, J.H., Benenson, R., Dollár, P., Schiele, B.: · 2016
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Cited alongside, same era.
Convolutional neural networks for direct text deblurring
Hradiš, M., Kotera, J., Zemcík, P., Šroubek, F.: · 2015
Cited alongside, same era.
Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
Nguyen, A., Yosinski, J., Clune, J.: · 2015
Cited alongside, same era.
Single image super-resolution from transformed self-exemplars
Huang, J.B., Singh, A., Ahuja, N.: · 2015
Cited alongside, same era.
Learning super-resolution jointly from external and internal examples
Wang, Z., Yang, Y., Wang, Z., Chang, S., Yang, J., Huang, T.S.: · 2015
Cited alongside, same era.
Going deeper with convolutions
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A.: · 2015
Cited alongside, same era.
Fast R-CNN
Girshick, R.B.: · 2015
Cited alongside, same era.
Later among the works it cites.
Region-based convolutional networks for accurate object detection and segmentation
Girshick, R.B., Donahue, J., Darrell, T., Malik, J.: · 2016
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Ssd: Single shot multibox detector
Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.Y., Berg, A.C.: · 2016
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Yolo9000: better, faster, stronger
Redmon, J., Farhadi, A.: · 2016
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Enhancenet: Single image super-resolution through automated texture synthesis
Sajjadi, M.S., Schölkopf, B., Hirsch, M.: · 2017
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Learning a no-reference quality metric for single-image super-resolution
Ma, C., Yang, C.Y., Yang, X., Yang, M.H.: · 2017
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Image quality assessment to enhance infrared face recognition
Pulecio, C.G.R., Benítez-Restrepo, H.D., Bovik, A.C.: · 2017
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Enhanced deep residual networks for single image super-resolution
Lim, B., Son, S., Kim, H., Nah, S., Lee, K.M.: · 2017
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Deep laplacian pyramid networks for fast and accurate super-resolution
Lai, W.S., Huang, J.B., Ahuja, N., Yang, M.H.: · 2017
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Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: · 2017
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Faster r-cnn: towards real-time object detection with region proposal networks
Ren, S., He, K., Girshick, R., Sun, J.: · 2017
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Mask r-cnn
He, K., Gkioxari, G., Dollár, P., Girshick, R.: · 2017
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Ntire 2017 challenge on single image super-resolution: Dataset and study
Agustsson, E., Timofte, R.: · 2017
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End-to-end blind image quality assessment using deep neural networks
Ma, K., Liu, W., Zhang, K., Duanmu, Z., Wang, Z., Zuo, W.: · 2018
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Pixelnn: Example-based image synthesis
Bansal, A., Sheikh, Y., Ramanan, D.: · 2018
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Deep back-projection networks for super-resolution
Haris, M., Shakhnarovich, G., Ukita, N.: · 2018
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Quality assessment for super-resolution image enhancement
Reibman, A.R., Bell, R.M., Gray, S.: · 2020
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Quality assessment for image super-resolution based on energy change and texture variation
Fang, Y., Liu, J., Zhang, Y., Lin, W., Guo, Z.: · 2061
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