Journal of Optoelectronics · Laser, Volume. 35, Issue 11, 1145(2024)
Lightweight image super-resolution reconstruction based on multi-scale key information fusion
[3] [3] DONG C, LOY C C, HE K, et al. Image super-resolution using deep convolutional networks[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 38(2): 295-307.
[4] [4] HE K, ZHANG X, REN S, et al. Deep residual learning for image recognition[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 770-778.
[5] [5] KIM J, LEE J K, LEE K M. Accurate image super-resolution using very deep convolutional networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 1646-1654.
[6] [6] KIM J, LEE J K, LEE K M. Deeply-recursive convolutional network for image super-resolution[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 1637-1645.
[7] [7] LIM B, SON S, KIM H, et al. Enhanced deep residual networks for single image super-resolution[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 136-144.
[8] [8] SHI W, CABALLERO J, HUSZR F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE, 2016: 1874-1883.
[9] [9] ZHANG Y, LI K, LI K, et al. Image super-resolution using very deep residual channel attention networks[C]//15th European Conference on Computer Vision, September 8-14, 2018, Munich, Germany. Berlin: Springer, 2018: 286-301.
[10] [10] HU J, SHEN L, SUN G. Squeeze-and-excitation networks[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 7132-7141.
[11] [11] ZHANG Y, TIAN Y, KONG Y, et al. Residual dense network for image super-resolution[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 2472-2481.
[12] [12] DONG C, LOY C C, TANG X. Accelerating the super-resolution convolutional neural network[C]//Computer Vision-ECCV 2016: 14th European Conference, Proceedings, Part II 14. October 11-14, 2016, Amsterdam, The Netherlands, Berlin: Springer, 2016: 391-407.
[13] [13] AHN N, KANG B, SOHN K A. Fast, accurate, and lightweight super-resolution with cascading residual network[C]//European Conference on Computer Vision, September 8-14, 2018, Munich, Germany. Berlin: Springer, 2018: 252-268.
[14] [14] HUI Z, WANG X, GAO X. Fast and accurate single image super-resolution via information distillation network[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 723-731.
[15] [15] ZHAO M, ZHONG S, FU X, et al. Deep residual shrinkage networks for fault diagnosis[J]. IEEE Transactions on Industrial Informatics, 2019, 16(7): 4681-4690.
[16] [16] WOO S, PARK J, LEE J Y, et al. CBAM: convolutional block attention module[C]//European Conference on Computer Vision, September 8-14, 2018, Munich, Germany. Heidelberg: Springer, 2018: 3-19.
[17] [17] AGUSTSSON E, TIMOFTE R. NTIRE 2017 challenge on single image super-resolution: Dataset and study[C]//IEEE Conference on Computer Vision and Pattern Recognition Workshops, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 126-135.
[18] [18] BEVILACQUA M, ROUMY A, GUILLEMOT C, et al. Low-complexity single-image super-resolution based on nonnegative neighbor embedding[C]//2012 British Machine Vision Conference, September 3-7, Surrey, Durham: BMVA Press, 2012: 135.
[19] [19] LEDIG C, THEIS L, HUSZAR F, et al. Photo-realistic single image super-resolution using a generative adversarial network[C]//IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 4681-4690.
[20] [20] ARBELAEZ P, MAIRE M, FOWLKES C, et al. Contour detection and hierarchical image segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 33(5): 898-916.
[21] [21] CAO H, MI S. Weighted SRGAN and reconstruction loss analysis for accurate image super resolution[J]. Journal of Physics: Conference Series, 2021, 1903(1): 012050.
[22] [22] LEI S, SHI Z, ZOU Z. Super-resolution for remote sensing images via local-global combined network[J]. IEEE Geoscience and Remote Sensing Letters, 2017, 14(8): 1243-1247.
[23] [23] LAI W S, HUANG J B, AHUJA N, et al. Deep Laplacian pyramid networks for fast and accurate super-resolution[C]//IEEE Conference on Computer Vision and Pattern Recognition, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE, 2017: 624-632.
[24] [24] TAI Y, YANG J, LIU X, et al. Memnet: A persistent memory network for image restoration[C]//IEEE International Conference on Computer Vision, July 21-26, 2017, Honolulu, HI, USA. New York: IEEE 2017: 4539-4547.
[25] [25] ZHANG K, ZUO W, ZHANG L. Learning a single convolutional super-resolution network for multiple degradations[C]//IEEE Conference on Computer Vision and Pattern Recognition, June 18-23, 2018, Salt Lake City, UT, USA. New York: IEEE, 2018: 3262-3271.
[26] [26] TIAN C, ZHUGE R, WU Z, et al. Lightweight image super-resolution with enhanced CNN[J]. Knowledge-Based Systems, 2020, 205: 106235.
[27] [27] ZHANG S, YUAN Q, LI J, et al. Scene-adaptive remote sensing image super-resolution using a multiscale attention network[J]. IEEE Transactions on Geoscience and Remote Sensing, 2020, 58(7): 4764-4779.
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LIU Yuanyuan, CHENG Shuangquan, ZHU Lu, WU Lei. Lightweight image super-resolution reconstruction based on multi-scale key information fusion[J]. Journal of Optoelectronics · Laser, 2024, 35(11): 1145
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Received: Mar. 23, 2023
Accepted: Dec. 31, 2024
Published Online: Dec. 31, 2024
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