Abstract
In multimedia devices such as mobile phones, surveillance cameras, and web cameras, image sensors have limited spatial resolution. As a result, the image captured from these devices misses high-frequency content and exhibits visual artifacts. Image super-resolution (SR) algorithms can minimize these artifacts by reconstructing missing high-frequency textures. Image SR algorithm estimates a high resolution (HR) image from a given low-resolution (LR) image. Given a single LR image, reconstructing an HR image makes SR be an extremely ill-posed problem. Over the past decade, dictionary learning-based methods have shown promising results in SR reconstruction. These methods extract numerous patches from external images for training dictionaries via sparse representation. However, these methods do not involve any patch selection mechanism that enhances the learning process. This paper proposes a dictionary learning-based SR algorithm that extracts selective patches from an input LR image based on the iScore criterion. Results show that patch selection criteria keep only 36% of all extracted patches for training while improving the peak signal-to-noise ratio (PSNR). Furthermore, we have proposed a method to initialize dictionaries to achieve better convergence that enhances PSNR.
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Data Availability
The datasets analysed during the current study are available in the GitHub repository, https://github.com/jbhuang0604/SelfExSR.
References
Alam MS, Bognar JG, Hardie RC, Yasuda BJ (2000) Infrared image registration and high-resolution reconstruction using multiple translationally shifted aliased video frames. IEEE Trans Instrument Measurement 49(5):915–923. https://doi.org/10.1109/19.872908
Amiri M, Ahmadyfard A, Abolghasemi V (2019) A fast video super resolution for facial image. Signal Process Image Commun 70:259–270
Baker S, Kanade T (2002) Limits on super-resolution and how to break them. IEEE Trans Pattern Anal Mach Intell 24(9):1167–1183. https://doi.org/10.1109/TPAMI.2002.1033210
Bevilacqua M, Roumy A, Guillemot C, Morel MLA (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. BMVA Press, https://doi.org/10.5244/C.26.135
Bevilacqua M, Roumy A, Guillemot C, Morel M (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding. In: Proceedings of the 23rd british machine vision conference (BMVC). BMVA press, pp 135.1–135.10
Cha Y, Kim S (2007) The error-amended sharp edge (ease) scheme for image zooming. IEEE Trans Image Process 16(6):1496–1505. https://doi.org/10.1109/TIP.2007.896645
Chang H, Yeung DY, Xiong Y (2004) Super-resolution through neighbor embedding. In: CVPR, vol 1, https://doi.org/10.1109/cvpr.2004.1315043
Choi J-S, Kim M (2017) Single image super-resolution using global regression based on multiple local linear mappings. IEEE Trans Image Process 26 (3):1300–1314
Christensen-Jeffries K, Couture O, Dayton PA, Eldar YC, Hynynen K, Kiessling F, O’Reilly M, Pinton GF, Schmitz G, Tang M-X et al (2020) Super-resolution ultrasound imaging. Ultrasound Med Biology 46 (4):865–891
Dai W, Xu T, Wang W (2012) Simultaneous codeword optimization (simco) for dictionary update and learning. IEEE Trans Signal Process 60 (12):6340–6353. https://doi.org/10.1109/TSP.2012.2215026
Dong C, Loy CC, He K, Tang X (2016) Image super-resolution using deep convolutional networks. IEEE Trans Pattern Anal Mach Intell 38 (2):295–307. https://doi.org/10.1109/TPAMI.2015.2439281
Elad M (2010) Sparse and redundant representations: from theory to applications in signal and image processing. Springer, https://doi.org/10.1007/978-1-4419-7011-4
Elad M, Figueiredo MA, Ma Y (2010) On the role of sparse and redundant representations in image processing. Proc IEEE 98(6):972–982. https://doi.org/10.1109/JPROC.2009.2037655
Elad M, Hel-Or Y (2001) A fast super-resolution reconstruction algorithm for pure translational motion and common space-invariant blur. IEEE Transa Image Process 10(8):1187–1193. https://doi.org/10.1109/83.935034
Farsiu S, Robinson MD, Elad M, Milanfar P (2004) Fast and robust multiframe super resolution. IEEE Trans Image Process 13(10):1327–1344. https://doi.org/10.1109/TIP.2004.834669
Freeman WT, Jones TR, Pasztor EC (2002) Example-based super-resolution. IEEE Comput Graph Appl 22(2):56–65. https://doi.org/10.1109/38.988747
Gao X, Zhang K, Tao D, Li X (2012) Image super-resolution with sparse neighbor embedding. IEEE Trans Image Process 21(7):3194–3205. https://doi.org/10.1109/TIP.2012.2190080
Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: 2009 IEEE 12Th international conference on computer vision, pp 349–356
Hardie RC, Barnard KJ, Armstrong EE (1997) Joint map registration and high-resolution image estimation using a sequence of undersampled images. IEEE Trans Image Process 6(12):1621–1633. https://doi.org/10.1109/83.650116
Irani M, Peleg S (1990) Super resolution from image sequences. In: [1990] Proceedings. 10th international conference on pattern recognition, vol ii, pp 115–120, https://doi.org/10.1109/icpr.1990.119340
Jiang K, Wang Z, Yi P, Jiang J (2020) Hierarchical dense recursive network for image super-resolution. Pattern Recognit 107:107475. https://doi.org/10.1016/j.patcog.2020.107475
Jiang K, Wang Z, Yi P, Wang G, Lu T, Jiang J (2019) Edge-enhanced GAN for remote sensing image superresolution. IEEE Trans Geosci Remote Sensing 57(8):5799–5812. https://doi.org/10.1109/TGRS.2019.2902431https://doi.org/10.1109/TGRS.2019.2902431
Jiang C, Zhang Q, Fan R, Hu Z (2018) Super-resolution ct image reconstruction based on dictionary learning and sparse representation. Sci Rep 8(1):1–10
Joshi M, Jalobeanu A (2009) Map estimation for multiresolution fusion in remotely sensed images using an igmrf prior model. IEEE Trans Geosci Remote Sens 48(3):1245–1255
Keren D, Peleg S, Brada R (1988) Image sequence enhancement using sub-pixel displacements. In: CVPR, vol 88, pp 5–9
Keys RG (1981) Cubic convolution interpolation for digital image processing. IEEE Trans Acoustics, Speech, Signal Process 29(6):1153–1160. https://doi.org/10.1109/TASSP.1981.1163711
Kim J, Lee JK, Lee KM (2016) Accurate image super-resolution using very deep convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1646–1654. https://doi.org/10.1109/CVPR.2016.182
Ledig C, Theis L, Huszár F, Caballero J, Cunningham A, Acosta A, Aitken A, Tejani A, Totz J, Wang Z et al (2017) Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4681–4690
Li X, Cao G, Zhang Y, Shafique A, Fu P (2020) Combining synthesis sparse with analysis sparse for single image super-resolution. Signal Process Image Commun 115805:83
Li X, Hu Y, Gao X, Tao D, Ning B (2010) A multi-frame image super-resolution method. Signal Process 90(2):405–414
Li M, Nguyen TQ (2008) Markov random field model-based edge-directed image interpolation. IEEE Trans Image Process 17(7):1121–1128. https://doi.org/10.1109/TIP.2008.924289
Li X, Orchard MT (2001) New edge-directed interpolation. IEEE Trans Image Process 10(10):1521–1527. https://doi.org/10.1109/83.951537
Merino MT, Nunez J (2007) Super-resolution of remotely sensed images with variable-pixel linear reconstruction. IEEE Trans Geosci Remote Sens 45 (5):1446–1457
Ning Q, Chen K, Yi L, Fan C, Lu Y, Wen J (2013) Image super-resolution via analysis sparse prior. IEEE Signal Process Lett 20 (4):399–402. https://doi.org/10.1109/LSP.2013.2242198
Patel R (2020) Image super-resolution through quick learning from self-examples. In: Technologies for Sustainable Development: proceedings of the 7th Nirma University international conference on engineering (NUiCONE 2019). CRC Press, pp 161–166
Patel R, Thakar V, Joshi R (2020) Single image super-resolution through sparse representation via coupled dictionary learning. Int J Electron Telecommun 66(2):347–353. https://doi.org/10.24425/ijet.2020.131884https://doi.org/10.24425/ijet.2020.131884
Peleg T, Elad M (2014) A statistical prediction model based on sparse representations for single image super-resolution. IEEE Trans Image Process 23(6):2569–2582. https://doi.org/10.1109/TIP.2014.2305844https://doi.org/10.1109/TIP.2014.2305844
Perez-Pellitero E, Salvador J, Ruiz-Hidalgo J, Rosenhahn B (2015) Accelerating super-resolution for 4k upscaling. In: 2015 IEEE international conference on consumer electronics (ICCE). IEEE, pp 317–320
Starck J-L, Murtagh F, Fadili JM (2010) Sparse Image and Signal Processing: wavelets, curvelets, morphological diversity. Cambridge university press. https://doi.org/10.1017/CBO9780511730344
Sun J, Zheng NN, Tao H, Shum HY (2003) Image hallucination with primal sketch priors. In: IEEE computer society conference on computer vision and pattern recognition. Proceedings, 2003, vol 2, pp II–729, https://doi.org/10.1109/cvpr.2003.1211539
Thévenaz P, Blu T, Unser M (2000) Image interpolation and resampling. Handbook Med Imaging, Process Anal 1(1):393–420
Timofte R, De Smet V, Van Gool L (2014) A+: Adjusted anchored neighborhood regression for fast super-resolution. In: Asian conference on computer vision. Springer, pp 111–126
Tipping M, Bishop C (2002) Bayesian Image Super-Resolution. In: S. Becker, S. Thrun, K. Obermayer (eds) Advances in Neural Information Processing Systems. MIT Press https://proceedings.neurips.cc/paper/2002/???le/88bfcf02e7f554f9e9ea350b699bc6a7-Paper.pdf, vol 15
Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inform Theory 53(12):4655–4666. https://doi.org/10.1109/TIT.2007.909108
Ur H, Gross D (1992) Improved resolution from subpixel shifted pictures. CVGIP: Graphical Models Image Process 54(2):181–186. https://doi.org/10.1016/1049-9652(92)90065-6
Van Reeth E, Tham IW, Tan CH, Poh CL (2012) Super-resolution in magnetic resonance imaging: a review. Concepts in Magnetic Resonance Part A 40(6):306–325
Wang Q, Tang X, Shum H (2005) Patch based blind image super resolution. In: Tenth ieee international conference on computer vision (ICCV’05), vol I, pp 709–716. https://doi.org/10.1109/ICCV.2005.186
Wang Z, Yi P, Jiang K, Jiang J, Han Z, Lu T, Ma J (2018) Multi-memory convolutional neural network for video super-resolution. IEEE Trans Image Process 28(5):2530–2544. https://doi.org/10.1109/TIP.2018.2887017https://doi.org/10.1109/TIP.2018.2887017
Wang S, Zhang L, Liang Y, Pan Q (2012) Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis. In: IEEE conference on computer vision and pattern recognition, pp 2216–2223. https://doi.org/10.1109/CVPR.2012.6247930
Yang J, Wright J, Huang TS, Ma Y (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873. https://doi.org/10.1109/TIP.2010.2050625
Yang W, Yuan T, Wang W, Zhou F, Liao Q (2017) Single-image super-resolution by subdictionary coding and kernel regression. IEEE Trans Syst, Man, Cybern: Syst 47(9):2478–2488. https://doi.org/10.1109/TSMC.2016.2523947https://doi.org/10.1109/TSMC.2016.2523947
Yedidia JS, Freeman WT, Weiss Y (2001) Generalized belief propagation. In: Advances in neural information processing systems, vol 13
Yi P, Wang Z, Jiang K, Jiang J, Lu T, Ma J (2022) A progressive fusion generative adversarial network for realistic and consistent video super-resolution. IEEE Trans Pattern Anal Mach Intell 44(5):2264–2280. https://doi.org/10.1109/TPAMI.2020.3042298
Zeyde R, Elad M, Protter M (2012) On single image scale-up using sparse-representations. In: On single image scale-up using sparse representations. LNCS, vol 6920, pp 711–730. https://doi.org/10.1007/978-3-642-27413-8_47
Zhang L, Wu X (2006) An edge-guided image interpolation algorithm via directional filtering and data fusion. IEEE Trans Image Process 15 (8):2226–2238. https://doi.org/10.1109/TIP.2006.877407
Zhang X, Wu X (2008) Image interpolation by adaptive 2-d autoregressive modeling and soft-decision estimation. IEEE Trans Image Process 17 (6):887–896. https://doi.org/10.1109/TIP.2008.924279
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Vishvjit Thakar and Rutvij Joshi contributed equally to this work.
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Patel, R., Thakar, V. & Joshi, R. Dictionary learning-based image super-resolution for multimedia devices. Multimed Tools Appl 82, 17243–17262 (2023). https://doi.org/10.1007/s11042-022-14076-4
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DOI: https://doi.org/10.1007/s11042-022-14076-4