Abstract
There are some problems with image super-resolution techniques, such as insufficient utilization of image features in different layers and lack high frequency information resulting in blurred texture edges in reconstructed images. A new method for the image super-resolution reconstruction of Widely Activated Network Fused Perceptual Loss (P-WAN) is proposed, which can improve the usage of image features from different layers. The method uses a pre-training model to extract features before activation to obtain the perceptual loss, meanwhile, draws on the adversarial loss in the adversarial generation network, and combines the pixel loss of the image to form a new loss function. Finally, we optimize the loss function by adjusting the weights of the three loss terms. Based on this research, a Widely Activated Network Based on Discrete Wavelet Transform and Fused Perceptual Loss (DP-WAN) is further proposed, which can reconstruct better high frequency information and higher quality texture edges. The method mainly adds the discrete wavelet transform on the basis of P-WAN, trains the different components obtained by the transform separately, and finally reconstructs the super-resolution image through the inverse discrete wavelet transform. To validate the feasibility and effectiveness, 4 representative methods are selected to test on 5 datasets. Experimental results have shown that the proposed method achieves the best performance in objective evaluation, and can obtain a good visual experience in subjective visual evaluation.
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Szegedy C, Vanhoucke V, Ioffe S, et al (2016) Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Conference on computer vision and pattern recognition, pp 2818–2826
Lei S, Shi Z, Zou Z (2019) Coupled adversarial training for remote sensing image super-resolution. IEEE Trans Geosci Remote Sens 58(5):3633–3643
Zhu J, Zeng H, Huang J et al (2019) Vehicle re-identification using quadruple directional deep learning features. IEEE Trans Intell Transp Syst 21(1):410–420
Chen J, Chen J, Wang Z et al (2020) Identity-aware face super-resolution for low-resolution face recognition. IEEE Signal Process Lett 27:645–649
Goyal B, Dogra A, Agrawal S et al (2020) Image denoising review: From classical to state-of-the-art approaches. Inform fusion 55:220–244
Rippel O, Bourdev L (2017) Real-time adaptive image compression. In: International Conference on machine learning. PMLR, pp 2922–2930
Dong W, Wang P, Yin W et al (2018) Denoising prior driven deep neural network for image restoration. IEEE Trans Pattern Anal Mach Intell 41(10):2305–2318
Yang J, Wright J, Huang TS et al (2010) Image super-resolution via sparse representation. IEEE Trans Image Process 19(11):2861–2873
Timofte R, Gu S, Wu J, et al (2018) Ntire 2018 challenge on single image super-resolution: methods and results. In: Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, pp 852–863
Kawulok M, Benecki P, Piechaczek S et al (2019) Deep learning for multiple-image super-resolution. IEEE Geosci Remote Sens Lett 17(6):1062–1066
Dou J, Tu Z, Peng X (2020) Single image super-resolution reconstruction with wavelet based deep residual learning. In: 2020 Chinese Control and Decision Conference (CCDC). IEEE, pp 4270–4275
Hao S, Dong X (2020) Interpolation-based plane stress anisotropic yield models. Int J Mech Sci 178:105612
Zhang Y, Li K, Li K, et al (2018) Image super-resolution using very deep residual channel attention networks. In: Proceedings of the European Conference on computer vision (ECCV), pp 286–301
Krizhevsky A, Sutskever I, Hinton GE (2017) Imagenet classification with deep convolutional neural networks. Communications of the ACM 60(6):84–90
Kimlyk M, Umnyashkin S (2018) Image denoising using discrete wavelet transform and edge information. In: 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). IEEE, pp 1823–1825
Dong C, Loy C C, He K, et al (2014) Learning a deep convolutional network for image super-resolution. In: European Conference on computer vision. Springer, Cham, pp 184–199
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
Lim B, Son S, Kim H, et al (2017) Enhanced deep residual networks for single image super-resolution. In: Proceedings of the IEEE Conference on computer vision and pattern recognition workshops, pp 136–144
Ting L (2019) Research on wavelet analysis and its application in image processing. In: 2019 International Conference on advanced manufacturing, computation and optimization. The Academy of Engineering and Education 1: 71–74
Ping Z, Jieqing T, Lei H (2007) Image inpainting method based on discrete wavelet transformation. Appl Res Comput 24(9):287–289
Mehta R, Rajpal N, Vishwakarma VP (2018) Robust image watermarking scheme in lifting wavelet domain using GA-LSVR hybridization. Int J Mach Learn Cybern 9(1):145–161
Khan H, Sharif M, Bibi N et al (2020) Localization of radiance transformation for image dehazing in wavelet domain. Neurocomputing 381:141–151
Waibel A, Hanazawa T, Hinton G et al (1989) Phoneme recognition using time-delay neural networks. IEEE Trans Acoust Speech Signal Process 37(3):328–339
LeCun Y, Bottou L, Bengio Y et al (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324
Hu T, Lin X, Wang X et al (2022) Supervised learning algorithm based on spike optimization mechanism for multilayer spiking neural networks. Int J Mach Learn Cybern 13(7):1981–1995
Fan Y, Shao M, Zuo W et al (2020) Unsupervised image-to-image translation using intra-domain reconstruction loss. Int J Mach Learn Cybern 11(9):2077–2088
Zhang Q, Zhang M, Chen T et al (2019) Recent advances in convolutional neural network acceleration. Neurocomputing 323:37–51
Ledig C, Theis L, Huszár F, et al. Photo-realistic single image super-resolution using a generative adversarial network. Proceedings of the IEEE conference on computer vision and pattern recognition. 2017: 4681–4690.
Yu J, Fan Y, Yang J, et al (2018) Wide activation for efficient and accurate image super-resolution. arXiv preprint arXiv:1808.08718
Dong C, Loy C, Tang X (2016) Accelerating the super-resolution convolutional neural network. In: European Conference on computer vision. Springer, Cham, pp 391–407
Mateo C, Talavera JA (2018) Short-time Fourier transform with the window size fixed in the frequency domain. Digital Signal Process 77:13–21
Liu Y, Guan L, Hou C et al (2019) Wind power short-term prediction based on LSTM and discrete wavelet transform. Appl Sci 9(6):1108
Wang X, Yu K, Wu S, et al (2018) Esrgan: Enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on computer vision (ECCV) workshops, pp 1–16
Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556
Johnson J, Alahi A, Fei-Fei L (2016) Perceptual losses for real-time style transfer and super-resolution. In: European Conference on computer vision. Springer, Cham, pp 694–711
Liu J, Zhang W, Tang Y, et al (2020) Residual feature aggregation network for image super-resolution. In: Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition, pp 2359–2368
Chatterjee S, Zielinski P (2022) On the generalization mystery in deep learning. arXiv preprint arXiv:2203.10036
Acknowledgements
This work is supported by the National Natural Science Foundations of China (no. 61976216, no. 62276265, and no. 61672522).
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Guo, L., Wang, Y., Wang, F. et al. Widely-activated network merging perceptual loss via discrete wavelet transform for image super-resolution. Int. J. Mach. Learn. & Cyber. 14, 2793–2813 (2023). https://doi.org/10.1007/s13042-023-01799-5
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DOI: https://doi.org/10.1007/s13042-023-01799-5