CLC number: TP391
On-line Access: 2023-07-24
Received: 2022-08-11
Revision Accepted: 2023-07-24
Crosschecked: 2022-11-28
Cited: 0
Clicked: 876
Zhixiong HUANG, Jinjiang LI, Zhen HUA, Linwei FAN. Filter-cluster attention based recursive network for low-light enhancement[J]. Frontiers of Information Technology & Electronic Engineering,in press.https://doi.org/10.1631/FITEE.2200344 @article{title="Filter-cluster attention based recursive network for low-light enhancement", %0 Journal Article TY - JOUR
基于过滤-群聚注意力的低光增强递归网络1山东工商学院信息与电子工程学院,中国烟台市,264005 2山东工商学院计算机科学与技术学院,中国烟台市,264005 3山东工商学院山东省高等学校未来智能计算协同创新中心,中国烟台市,264005 4山东财经大学计算机科学与技术学院,中国济南市,250014 摘要:在低光环境下拍摄的图像质量不佳,影响其进一步应用。为提升低光图像可视性,提出一种基于过滤-群聚注意力(FCA)的递归网络,其中主体由3个单元组成:差异关注、门控递归以及迭代残差。该网络对低光图像进行多阶段递归学习,进而提取更深层次特征信息。为算得更加精确的相关性,设计了一种关注特征通道突出性的FCA。FCA与自注意力被用以突出特征的低光区域与重要通道。此外,设计了密集连接金字塔(DenCP)来提取低光反转图的色彩特征,使图像的色彩信息损失得以补偿。在6种公开数据集上的实验结果表明,本文方法在视觉和指标上有着突出表现。 关键词组: Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article
Reference[1]Abdullah-Al-Wadud M, Kabir H, Dewan MAA, et al., 2007. A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron, 53(2):593-600. [2]Aradi S, 2022. Survey of deep reinforcement learning for motion planning of autonomous vehicles. IEEE Trans Intell Transp Syst, 23(2):740-759. [3]Bychkovsky V, Paris S, Chan E, et al., 2011. Learning photographic global tonal adjustment with a database of input/output image pairs. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.97-104. [4]Celik T, Tjahjadi T, 2011. Contextual and variational contrast enhancement. IEEE Trans Image Process, 20(12):3431-3441. [5]Chen BH, Wu YL, Shi LF, 2019. A fast image contrast enhancement algorithm using entropy-preserving mapping prior. IEEE Trans Circ Syst Video Technol, 29(1):38-49. [6]Cheng HD, Shi XJ, 2004. A simple and effective histogram equalization approach to image enhancement. Dig Signal Process, 14(2):158-170. [7]Cho K, van Merriënboer B, Gulcehre C, et al., 2014. Learning phrase representations using RNN encoder-decoder for statistical machine translation. Proc Conf on Empirical Methods in Natural Language Processing, p.1724-1734. [8]Guo CL, Li CY, Guo JC, et al., 2020. Zero-reference deep curve estimation for low-light image enhancement. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.1780-1789. [9]Guo XJ, Li Y, Ling HB, 2017. LIME: low-light image enhancement via illumination map estimation. IEEE Trans Image Process, 26(2):982-993. [10]Hao SJ, Han X, Guo YR, et al., 2020. Low-light image enhancement with semi-decoupled decomposition. IEEE Trans Multim, 22(12):3025-3038. [11]Hochreiter S, Schmidhuber J, 1997. Long short-term memory. Neur Comput, 9(8):1735-1780. [12]Huang G, Liu Z, van der Maaten L, et al., 2017. Densely connected convolutional networks. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.2261-2269. [13]Huang ZX, Li JJ, Hua Z, et al., 2022. Underwater image enhancement via adaptive group attention-based multiscale cascade transformer. IEEE Trans Instrum Meas, 71:5015618. [14]Jiang YF, Gong XY, Liu D, et al., 2021. EnlightenGAN: deep light enhancement without paired supervision. IEEE Trans Image Process, 30:2340-2349. [15]Jung E, Yang N, Cremers D, 2020. Multi-frame GAN: image enhancement for stereo visual odometry in low light. Proc 3rd Annual Conf on Robot Learning, p.651-660. [16]Kingma DP, Ba J, 2014. Adam: a method for stochastic optimization. Proc 3rd Int Conf on Learning Representations. [17]Lee C, Lee C, Kim CS, 2012. Contrast enhancement based on layered difference representation. 19th IEEE Int Conf on Image Processing, p.965-968. [18]Lee C, Lee C, Kim CS, 2013. Contrast enhancement based on layered difference representation of 2D histograms. IEEE Trans Image Process, 22(12):5372-5384. [19]Li CL, Tang SQ, Yan JW, et al., 2020. Low-light image enhancement based on quasi-symmetric correction functions by fusion. Symmetry, 12(9):1561. [20]Li JJ, Feng XM, Hua Z, 2021. Low-light image enhancement via progressive-recursive network. IEEE Trans Circ Syst Video Technol, 31(11):4227-4240. [21]Li L, Wang RG, Wang WM, et al., 2015. A low-light image enhancement method for both denoising and contrast enlarging. IEEE Int Conf on Image Processing, p.3730-3734. [22]Li MD, Liu JY, Yang WH, et al., 2018. Structure-revealing low-light image enhancement via robust retinex model. IEEE Trans Image Process, 27(6):2828-2841. [23]Li PL, Liang JL, Zhang MH, 2021. A degradation model for simultaneous brightness and sharpness enhancement of low-light image. Signal Process, 189:108298. [24]Lim KL, Jiang XD, Yi CY, 2020. Deep clustering with variational autoencoder. IEEE Signal Process Lett, 27:231-235. [25]Liu L, Ouyang WL, Wang XG, et al., 2020. Deep learning for generic object detection: a survey. Int J Comput Vis, 128(2):261-318. [26]Liu RS, Ma L, Zhang JA, et al., 2021. Retinex-inspired unrolling with cooperative prior architecture search for low-light image enhancement. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.10556-10565. [27]Liu YJ, Wang ZN, Zeng Y, et al., 2021. PD-GAN: perceptual-details GAN for extremely noisy low light image enhancement. IEEE Int Conf on Acoustics, Speech and Signal Processing, p.1840-1844. [28]Loh YP, Chan CS, 2019. Getting to know low-light images with the exclusively dark dataset. Comput Vis Image Underst, 178:30-42. [29]Lore KG, Akintayo A, Sarkar S, 2017. LLNet: a deep autoencoder approach to natural low-light image enhancement. Patt Recogn, 61:650-662. [30]Lv FF, Li Y, Lu F, 2021. Attention guided low-light image enhancement with a large scale low-light simulation dataset. Int J Comput Vis, 129(7):2175-2193. [31]Ma L, Liu RS, Zhang JA, et al., 2022. Learning deep context-sensitive decomposition for low-light image enhancement. IEEE Trans Neur Netw Learn Syst, 33(10):5666-5680. [32]Mittal A, Soundararajan R, Bovik AC, 2013. Making a “completely blind” image quality analyzer. IEEE Signal Process Lett, 20(3):209-212. [33]Peng T, Su LL, Zhang RH, et al., 2020. A new safe lane-change trajectory model and collision avoidance control method for automatic driving vehicles. Expert Syst Appl, 141:112953. [34]Ren WQ, Liu SF, Ma L, et al., 2019. Low-light image enhancement via a deep hybrid network. IEEE Trans Image Process, 28(9):4364-4375. [35]Ren XT, Li MD, Cheng WH, et al., 2018. Joint enhancement and denoising method via sequential decomposition. IEEE Int Symp on Circuits and Systems, p.1-5. [36]Shiau YH, Chen PY, Yang HY, et al., 2015. A low-cost hardware architecture for illumination adjustment in real-time applications. IEEE Trans Intell Transp Syst, 16(2):934-946. [37]Singh H, Kumar A, Balyan LK, et al., 2017. A novel optimally gamma corrected intensity span maximization approach for dark image enhancement. 22nd Int Conf on Digital Signal Processing, p.1-5. [38]Singh N, Bhandari AK, 2021. Principal component analysis-based low-light image enhancement using reflection model. IEEE Trans Instrum Meas, 70:70:5012710. [39]Wang LW, Liu ZS, Siu WC, et al., 2020. Lightening network for low-light image enhancement. IEEE Trans Image Process, 29:7984-7996. [40]Wang QL, Wu BG, Zhu PF, et al., 2020. ECA-Net: efficient channel attention for deep convolutional neural networks. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.11531-11539. [41]Wang SH, Luo G, 2018. Naturalness preserved image enhancement using a priori multi-layer lightness statistics. IEEE Trans Image Process, 27(2):938-948. [42]Wang SH, Zheng J, Hu HM, et al., 2013. Naturalness preserved enhancement algorithm for non-uniform illumination images. IEEE Trans Image Process, 22(9):3538-3548. [43]Wang W, Sun N, Ng MK, 2019. A variational gamma correction model for image contrast enhancement. Inv Probl Imag, 13(3):461-478. [44]Wang YF, Liu HM, Fu ZW, 2019. Low-light image enhancement via the absorption light scattering model. IEEE Trans Image Process, 28(11):5679-5690. [45]Wei C, Wang WJ, Yang WH, et al., 2018. Deep retinex decomposition for low-light enhancement. British Machine Vision Conf, Article 155. [46]Wu XM, Liu XH, Hiramatsu K, et al., 2017. Contrast-accumulated histogram equalization for image enhancement. IEEE Int Conf on Image Processing, p.3190-3194. [47]Xie EZ, Ding J, Wang WH, et al., 2021. DetCo: unsupervised contrastive learning for object detection. IEEE/CVF Int Conf on Computer Vision, p.8372-8381. [48]Xu CR, Peng ZZ, Hu XZ, et al., 2020. FPGA-based low-visibility enhancement accelerator for video sequence by adaptive histogram equalization with dynamic clip-threshold. IEEE Trans Circ Syst I Regul Papers, 67(11):3954-3964. [49]Xu K, Yang X, Yin BC, et al., 2020. Learning to restore low-light images via decomposition-and-enhancement. IEEE/CVF Conf on Computer Vision and Pattern Recognition, p.2278-2287. [50]Xu YD, Yang C, Sun BB, et al., 2021. A novel multi-scale fusion framework for detail-preserving low-light image enhancement. Inform Sci, 548:378-397. [51]Yan XA, Liu Y, Jia MP, 2020a. Multiscale cascading deep belief network for fault identification of rotating machinery under various working conditions. Knowl-Based Syst, 193:105484. [52]Yan XA, Liu Y, Xu YD, et al., 2020b. Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition. Energy Conv Manag, 225:113456. [53]Yang B, Cao XL, Yuen C, et al., 2021. Offloading optimization in edge computing for deep-learning-enabled target tracking by Internet of UAVs. IEEE Int Things J, 8(12):9878-9893. [54]Yang WH, Wang WJ, Huang HF, et al., 2021a. Sparse gradient regularized deep retinex network for robust low-light image enhancement. IEEE Trans Image Process, 30:2072-2086. [55]Yang WH, Wang SQ, Fang YM, et al., 2021b. Band representation-based semi-supervised low-light image enhancement: bridging the gap between signal fidelity and perceptual quality. IEEE Trans Image Process, 30:3461-3473. [56]Ying ZQ, Li G, Ren YR, et al., 2017. A new low-light image enhancement algorithm using camera response model. IEEE Int Conf on Computer Vision Workshops, p.3015-3022. [57]Yu SY, Zhu H, 2019. Low-illumination image enhancement algorithm based on a physical lighting model. IEEE Trans Circ Syst Video Technol, 29(1):28-37. [58]Zamir SW, Arora A, Khan S, et al., 2020. Learning enriched features for real image restoration and enhancement. Proc 16th European Conf on Computer Vision, p.492-511. [59]Zhang L, Zhang L, Mou XQ, et al., 2011. FSIM: a feature similarity index for image quality assessment. IEEE Trans Image Process, 20(8):2378-2386. [60]Zhang TL, Li JJ, Fan H, 2022. Progressive edge-sensing dynamic scene deblurring. Comput Visual Media, 8(3):495-508. [61]Zhang YH, Zhang JW, Guo XJ, 2019. 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