• Laser & Optoelectronics Progress
  • Vol. 59, Issue 8, 0810018 (2022)
Shuang Luo1, Hui Huang2、*, and Kaibing Zhang1
Author Affiliations
  • 1School of Electronics and Information, Xi'an Polytechnic University, Xi'an , Shaanxi 710048, China
  • 2School of Vocational and Technical Education, Guangxi Science & Technology Normal University, Laibin , Guangxi 546199, China
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    DOI: 10.3788/LOP202259.0810018 Cite this Article Set citation alerts
    Shuang Luo, Hui Huang, Kaibing Zhang. Boosting Regression-Based Single-Image Super-Resolution Reconstruction[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0810018 Copy Citation Text show less
    References

    [1] Zhang L, Wu X L. An edge-guided image interpolation algorithm via directional filtering and data fusion[J]. IEEE Transactions on Image Processing, 15, 2226-2238(2006).

    [2] Ye W, Ma K K. Convolutional edge diffusion for fast contrast-guided image interpolation[J]. IEEE Signal Processing Letters, 23, 1260-1264(2016).

    [3] Ren C, He X H, Teng Q Z et al. Single image super-resolution using local geometric duality and non-local similarity[J]. IEEE Transactions on Image Processing, 25, 2168-2186(2016).

    [4] Chen H G, He X H, Qing L B et al. Single image super-resolution via adaptive transform-based nonlocal self-similarity modeling and learning-based gradient regularization[J]. IEEE Transactions on Multimedia, 19, 1702-1717(2017).

    [5] Zeyde R, Elad M, Protter M. On single image scale-up using sparse-representations[M]. Boissonnat J D, Chenin P, Cohen A, et al. Curves and surfaces 2010. Lecture notes in computer science, 6920, 711-730(2012).

    [6] Li B, Ma L. Super-resolution reconstruction of densely connected generative adversarial network images[J]. Laser & Optoelectronics Progress, 57, 221011(2020).

    [7] Gao X B, Zhang K B, Tao D C et al. Image super-resolution with sparse neighbor embedding[J]. IEEE Transactions on Image Processing, 21, 3194-3205(2012).

    [8] Yang S Y, Wang Z Y, Zhang L et al. Dual-geometric neighbor embedding for image super resolution with sparse tensor[J]. IEEE Transactions on Image Processing, 23, 2793-2803(2014).

    [9] Yang J C, Wright J, Huang T S et al. Image super-resolution via sparse representation[J]. IEEE Transactions on Image Processing, 19, 2861-2873(2010).

    [10] Wang S L, Zhang L, Liang Y et al. Semi-coupled dictionary learning with applications to image super-resolution and photo-sketch synthesis[C], 2216-2223(2012).

    [11] Chen X Y, Zhang W J, Sun W Z et al. Super-resolution reconstruction of images based on multi-scale and multi-residual network[J]. Laser & Optoelectronics Progress, 57, 181009(2020).

    [12] Wu L, Lü G Q, Xue Z T et al. Super-resolution reconstruction of images based on multi-scale recursive network[J]. Acta Optica Sinica, 39, 0610001(2019).

    [13] Timofte R, de Smet V, van Gool L. Anchored neighborhood regression for fast example-based super-resolution[C], 1920-1927(2013).

    [14] Timofte R, de Smet V, van Gool L. A+: adjusted anchored neighborhood regression for fast super-resolution[M]. Cremers D, Reid I, Saito H, et al. Computer vision-ACCV 2014. Lecture notes in computer science, 9006, 111-126(2015).

    [15] Zhang K B, Tao D C, Gao X B et al. Learning multiple linear mappings for efficient single image super-resolution[J]. IEEE Transactions on Image Processing, 24, 846-861(2015).

    [16] Hu Y T, Wang N N, Tao D C et al. SERF: a simple, effective, robust, and fast image super-resolver from cascaded linear regression[J]. IEEE Transactions on Image Processing, 25, 4091-4102(2016).

    [17] Huang Y F, Li J, Gao X B et al. Single image super-resolution via multiple mixture prior models[J]. IEEE Transactions on Image Processing, 27, 5904-5917(2018).

    [18] Chen Z H, Wu H B, Pei H D et al. Image super-resolution reconstruction method based on self-attention deep network[J]. Laser & Optoelectronics Progress, 58, 0410013(2021).

    [19] Qu H C, Tang B W, Yuan G S. Improved super-resolution image reconstruction algorithm[J]. Laser & Optoelectronics Progress, 58, 0210018(2021).

    [20] Dong C, Loy C C, He K M et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 38, 295-307(2016).

    [21] Liu D, Wang Z W, Wen B H et al. Robust single image super-resolution via deep networks with sparse prior[J]. IEEE Transactions on Image Processing, 25, 3194-3207(2016).

    [22] Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C], 1646-1654(2016).

    [23] Ledig C, Theis L, Huszár F et al. Photo-realistic single image super-resolution using a generative adversarial network[C], 105-114(2017).

    [24] Lim B, Son S, Kim H et al. Enhanced deep residual networks for single image super-resolution[C], 1132-1140(2017).

    [25] Jiang J J, Ma X, Chen C et al. Single image super-resolution via locally regularized anchored neighborhood regression and nonlocal means[J]. IEEE Transactions on Multimedia, 19, 15-26(2017).

    [26] Wu H P, Zhang J, Wei Z H. High resolution similarity directed adjusted anchored neighborhood regression for single image super-resolution[J]. IEEE Access, 6, 25240-25247(2018).

    [27] Zhang K B, Wang Z, Li J et al. Learning recurrent residual regressors for single image super-resolution[J]. Signal Processing, 154, 324-337(2019).

    [28] Nejati M, Samavi S, Karimi N et al. Boosted dictionary learning for image compression[J]. IEEE Transactions on Image Processing, 25, 4900-4915(2016).

    [29] Cheng D Q, Yu W J, Guo X et al. Super-resolution reconstruction algorithm based on adaptive image online dictionary learning[J]. Laser & Optoelectronics Progress, 57, 061505(2020).

    [30] Zhang G Q, Porikli F, Sun H J et al. Cost-sensitive joint feature and dictionary learning for face recognition[J]. Neurocomputing, 391, 177-188(2020).

    [31] Aharon M, Elad M, Bruckstein A. K-SVD: an algorithm for designing overcomplete dictionaries for sparse representation[J]. IEEE Transactions on Signal Processing, 54, 4311-4322(2006).

    [32] Tropp J A, Gilbert A C. Signal recovery from random measurements via orthogonal matching pursuit[J]. IEEE Transactions on Information Theory, 53, 4655-4666(2007).

    [33] Golub G H, Hansen P C, O’Leary D P. Tikhonov regularization and total least squares[J]. SIAM Journal on Matrix Analysis and Applications, 21, 185-194(1999).

    Shuang Luo, Hui Huang, Kaibing Zhang. Boosting Regression-Based Single-Image Super-Resolution Reconstruction[J]. Laser & Optoelectronics Progress, 2022, 59(8): 0810018
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