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Light-weight Super-Resolution Network based on Classified Measurement-domain Features

Published: 14 June 2024 Publication History

Abstract

In contemporary research, natural images are frequently reconstructed using methods that are computationally intensive but highly effective. To solve this issue, we propose a light-weight super-resolution(SR) network based on classified measurement-domain features, abbreviated as SRCMF. Images of nature can be divided into multiple smaller images for processing purposes. On the basis of this line of reasoning, it is possible to conclude that various image regions present unique recovery challenges and can be processed by models with differing degrees of complexity. Textured regions make supersegmentation more difficult than flat regions. To take advantage of this property, we can manage the various subgraphs of the split using an appropriate SR model, i.e. a simple small model for smooth regions and a complex large model for textured regions. With this as our starting point, we propose the SRCMF, a classification and supersegmentation integrating framework. Specifically, it employs Class Modules to categorize subgraphs based on their recovery difficulty, followed by numerous SRModules to super-score the subgraphs in each category. The computational cost can be drastically reduced because the preponderance of sub-images will travel through fewer networks. ClassSR is founded on a compression-aware image block processing method on the measurement domain, while SRModule is comprised of SR models of varying proportions. When reconstructing natural images, experimental data indicate that our SRCMF can save FLOPs and reduce the computational complexity of reconstruction. This general structure is applicable to additional simple visual duties.

References

[1]
Yue L, Shen H, Li J, Image super-resolution: The techniques, applications, and future[J]. Signal processing, 2016, 128: 389-408.
[2]
Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. Springer International Publishing, 2016: 391-407.
[3]
Kong X, Zhao H, Qiao Y, Classsr: A general framework to accelerate super-resolution networks by data characteristic[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 2021: 12016-12025.
[4]
Aggarwal A, Kumar M. Image surface texture analysis and classification using deep learning[J]. Multimedia Tools and Applications, 2021, 80: 1289-1309.
[5]
Henderson C R. Estimation of variance and covariance components[J]. Biometrics, 1953, 9(2): 226-252.
[6]
Wellens M, Riihijärvi J, Mähönen P. Empirical time and frequency domain models of spectrum use[J]. Physical Communication, 2009, 2(1-2): 10-32.
[7]
Wang B, Zou Y, Zhang L, Low-light-level image super-resolution reconstruction based on a multi-scale features extraction network[C]//Photonics. MDPI, 2021, 8(8): 321.
[8]
Dong C, Loy C C, He K, Image super-resolution using deep convolutional networks[J]. IEEE transactions on pattern analysis and machine intelligence, 2015, 38(2): 295-307.
[9]
Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1646-1654.
[10]
Ledig C, Theis L, Huszár F, Photo-realistic single image super-resolution using a generative adversarial network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4681-4690.
[11]
He K, Zhang X, Ren S, Deep residual learning for image recognition[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 770-778.
[12]
Dong C, Loy C C, Tang X. Accelerating the super-resolution convolutional neural network[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part II 14. Springer International Publishing, 2016: 391-407.
[13]
Shi W, Caballero J, Huszár F, Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2016: 1874-1883.
[14]
Lai W S, Huang J B, Ahuja N, Deep laplacian pyramid networks for fast and accurate super-resolution[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 624-632.
[15]
Ahn N, Kang B, Sohn K A. Fast, accurate, and lightweight super-resolution with cascading residual network[C]//Proceedings of the European conference on computer vision (ECCV). 2018: 252-268.
[16]
Hui Z, Gao X, Yang Y, Lightweight image super-resolution with information multi-distillation network[C]//Proceedings of the 27th acm international conference on multimedia. 2019: 2024-2032.
[17]
Zhao H, Kong X, He J, Efficient image super-resolution using pixel attention[C]//Computer Vision–ECCV 2020 Workshops: Glasgow, UK, August 23–28, 2020, Proceedings, Part III 16. Springer International Publishing, 2020: 56-72.
[18]
Romano Y, Isidoro J, Milanfar P. RAISR: rapid and accurate image super resolution[J]. IEEE Transactions on Computational Imaging, 2016, 3(1): 110-125.
[19]
Wang X, Yu K, Dong C, Recovering realistic texture in image super-resolution by deep spatial feature transform[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 606-615.
[20]
Yu K, Dong C, Lin L, Crafting a toolchain for image restoration by deep reinforcement learning[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 2443-2452.
[21]
Yu K, Wang X, Dong C, Path-restore: Learning network path selection for image restoration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021, 44(10): 7078-7092.
[22]
Shi W, Jiang F, Liu S, Image compressed sensing using convolutional neural network[J]. IEEE Transactions on Image Processing, 2019, 29: 375-388.
[23]
Wang X, Yu K, Wu S, Esrgan: Enhanced super-resolution generative adversarial networks[C]//Proceedings of the European conference on computer vision (ECCV) workshops. 2018:1-8.
[24]
Candès E J. Compressive sampling[C]//Proceedings of the international congress of mathematicians. 2006, 3: 1433-1452.
[25]
Shi W, Jiang F, Liu S, Image compressed sensing using convolutional neural network[J]. IEEE Transactions on Image Processing, 2019, 29: 375-388.
[26]
Zhou S, He Y, Liu Y, Multi-channel deep networks for block-based image compressive sensing[J]. IEEE Transactions on Multimedia, 2020, 23: 2627-2640.
[27]
Luo Z, Yu J, Liu Z. The super‐resolution reconstruction of SAR image based on the improved FSRCNN[J]. The Journal of Engineering, 2019, 2019(19): 5975-5978.
[28]
Zulkifli N A B, Karim S A A, Shafie A B, Image interpolation using a rational bi-cubic ball[J]. Mathematics, 2019, 7(11): 1045.
[29]
Bevilacqua M, Roumy A, Guillemot C, Low-complexity single-image super-resolution based on nonnegative neighbor embedding[J]. 2012.
[30]
Yang J, Wright J, Huang T S, Image super-resolution via sparse representation[J]. IEEE transactions on image processing, 2010, 19(11): 2861-2873

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AIPR '23: Proceedings of the 2023 6th International Conference on Artificial Intelligence and Pattern Recognition
September 2023
1540 pages
ISBN:9798400707674
DOI:10.1145/3641584
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Published: 14 June 2024

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