[1] R S Lan, L Sun, Z B Liu et al. MADNet: a fast and lightweight network for single-image super resolution. IEEE Trans Cybern, 51, 1443-1453(2021).

[2] J P Lin, L Z Liao, S L Lin et al. Deep and adaptive feature extraction attention network for single image super-resolution. J Soc Inf Disp, 32, 23-33(2024).

[3] S J Xu, H Yang, M H Li et al. Low-light image enhancement based on dual-frequency domain feature aggregation. Opto-Electron Eng, 50, 230225(2023).

[4] G H Liu, Q Yang, Y B Meng et al. A progressive fusion image enhancement method with parallel hybrid attention. Opto-Electron Eng, 50, 220231(2023).

[5] X Jin, L H Han, Z Li et al. DNF: decouple and feedback network for seeing in the dark, 18135-18144(2023). https://doi.org/10.1109/CVPR52729.2023.01739

[6] K G Lore, A Akintayo, S Sarkar. LLNet: a deep autoencoder approach to natural low-light image enhancement. Pattern Recognit, 61, 650-662(2017).

[7] C Wei, W J Wang, W H Yang et al. Deep retinex decomposition for low-light enhancement, 155(2018).

[8] X J Guo, Y Li, H B Ling. LIME: low-light image enhancement via illumination map estimation. IEEE Trans Image Process, 26, 982-993(2017).

[9] Y F Gong, P Y Liao, X D Zhang et al. Enlighten-GAN for super resolution reconstruction in mid-resolution remote sensing images. Remote Sens, 13, 1104(2021).

[10] L Ma, T Y Ma, R S Liu et al. Toward fast, flexible, and robust low-light image enhancement, 5627-5636(2022). https://doi.org/10.1109/CVPR52688.2022.00555

[11] Z Q Fu, Y Yang, X T Tu et al. Learning a simple low-light image enhancer from paired low-light instances, 22252-22261(2023). https://doi.org/10.1109/CVPR52729.2023.02131

[12] D Liang, Z Y Xu, L Li et al. PIE: physics-inspired low-light enhancement(2024). https://arxiv.org/abs/2404.04586

[13] J P Lin, D Wang, Z Y Xiao et al. Minimum spanning tree segmentation and extract with image edge weight optimization. J Electron Inf Technol, 45, 1494-1504(2023).

[14] D Q Cheng, Y Y You, Q Q Kou et al. A generative adversarial network incorporating dark channel prior loss used for single image defogging. Opto-Electron Eng, 49, 210448(2022).

[15] H X Liu, S L Lin, Z X Lin et al. Lightweight underwater image enhancement network based on GAN. Chin J Liq Cryst Disp, 38, 378-386(2023).

[16] Y H Cai, H Bian, J Lin et al. Retinexformer: one-stage retinex-based transformer for low-light image enhancement, 12470-12479(2023). https://doi.org/10.1109/ICCV51070.2023.01149

[17] O Ronneberger, P Fischer, T Brox. U-Net: convolutional networks for biomedical image segmentation, 234-241(2015). https://doi.org/10.1007/978-3-319-24574-4_28

[18] A A Joshy, R Rajan. Dysarthria severity assessment using squeeze-and-excitation networks. Biomed Signal Process Control, 82, 104606(2023).

[19] S Zhang, Z W Liu, Y P Chen et al. Selective kernel convolution deep residual network based on channel-spatial attention mechanism and feature fusion for mechanical fault diagnosis. ISA Transactions, 133, 369-383(2023).

[20] N Ponomarenko, F Silvestri, K Egiazarian et al. On between-coefficient contrast masking of DCT basis functions, 1-4(2007).

[21] Z Wang, A C Bovik, H R Sheikh et al. Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process, 13, 600-612(2004).

[22] R Zhang, P Isola, A A Efros et al. The unreasonable effectiveness of deep features as a perceptual metric, 586-595(2018). https://doi.org/10.1109/CVPR.2018.00068

[23] L Zhang, L Zhang, A C Bovik. A feature-enriched completely blind image quality evaluator. IEEE Trans Image Process, 24, 2579-2591(2015).

[24] J Hai, Z Xuan, R Yang et al. R2RNet: low-light image enhancement via real-low to real-normal network. J Vis Commun Image Represent, 90, 103712(2023).

[25] C Lee, C Lee, C S Kim. Contrast enhancement based on layered difference representation, 965-968(2012). https://doi.org/10.1109/ICIP.2012.6467022

[26] K D Ma, K Zeng, Z Wang. Perceptual quality assessment for multi-exposure image fusion. IEEE Trans Image Process, 24, 3345-3356(2015).

[27] S H Wang, J Zheng, H M Hu et al. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans Image Process, 22, 3538-3548(2013).

[28] W H Wu, J Weng, P P Zhang et al. URetinex-Net: retinex-based deep unfolding network for low-light image enhancement, 5891-5900(2022). https://doi.org/10.1109/CVPR52688.2022.00581

[29] R S Liu, L Ma, J A Zhang et al. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement, 10556-10565(2021). https://doi.org/10.1109/CVPR46437.2021.01042