计算机科学 ›› 2024, Vol. 51 ›› Issue (5): 117-124.doi: 10.11896/jsjkx.230300049
周宇1, 陈志华1, 盛斌2, 梁磊1
ZHOU Yu1, CHEN Zhihua1, SHENG Bin2, LIANG Lei1
摘要: 现有的去雾方法难以在复原图像细节的同时保持全局信息。为了解决此问题,文中提出了一种基于渐进式多尺度Transformer(Multi Scale Progressive Transformer,MSP-Transformer)的图像去雾算法。该模型能够有效提取和利用不同尺度的雾相关特征,实现了特征和图像的多尺度学习和融合,渐进式地从有雾图像中复原清晰图像。所提出的MSP-Transformer分为编码、解码和复原3个阶段。在编码阶段,利用基于Transformer模块的编码器将输入图像分解为不同尺度的雾图像特征,以全面表征真实有雾图像的信息损失。在解码阶段,考虑到有雾图像的不同区域存在不同尺度的信息丢失,设计了一个包含多尺度注意力机制的特征聚合模块,利用通道注意力和多尺度空间注意力来融合不同尺度的特征信息。复原阶段包含了复原模块和融合模块,首先基于多尺度特征融合的复原模块聚合不同尺度的雾相关特征以增加不同尺度特征的联系,并在每个尺度复原出清晰的无雾图像,然后将每个尺度的复原图像送入融合模块以获得最终的去雾结果。定性和定量的实验结果表明,所提出的MSP-Transformer在真实图像和合成数据集上能够实现雾的有效去除,具有良好的鲁棒性。在公开的RESIDE数据集上与11种去雾方法进行定量和定性比较,MSP-Transformer取得了最高的PSNR(39.53db)和SSIM(0.9954),并获得了良好的视觉效果。此外,消融实验也证明了MSP-Transformer中所提出的模块的有效性。
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[1]HE K M,SUN J,TANG X.Single image haze removal usingdark channel prior[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2011,33(12):2341-2353. [2]ZHU Q S,MAI J M,SHAO L.A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior[J].IEEE Transactions on Image Processing,2015,24(11):3522-3533. [3]BERMAN D,TREIBITZ T,AVIDAN S.2020.Single Image Dehazing Using Haze-Lines[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2020,42(3):720-734. [4]ZHANG J L,SHI D Y,JIA B.Insulator image defogging algorithm based on dark channel prior theory[J].Journal of Chongqing University of Technology(Natural Science),2022,36(7):208-215. [5]QIN X,WANG Z L,BAI Y C,et al.FFA-Net:Feature Fusion Attention Network for Single Image Dehazing[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2020,34:11908-11915. [6]ZHANG J L,SHI D Y,JIA B.Insulator image defogging algorithm based on dark channel prior theory[J].Journal of Chongqing University of Technology(Natural Science),2022,36(7):208-215. [7]LIU X H,MA Y R,SHI Z H,et al.GridDehazeNet:Attention-Based Multi-Scale Network for Image Dehazing[C]//Procee-dings of the IEEE/CVF International Conference on Computer Vision.2019:7313-7322. [8]VASWANI A,SHAZEER N,PARMER N,et al.Attention is all you need[C]//Neural Information Processing Systems.2017:5998-6008. [9]ZAMIR S W,ARORA A,KHAN S,et al.Restormer:Efficient Transformer for High-Resolution Image Restoration[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:5718-5729. [10]WANG Z D,CUN X D,BAO J M,et al.Uformer:A GeneralU-Shaped Transformer for Image Restoration[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2022:17662-17672. [11]SONG Y,HE Z Q,QIAN H,et al.2022.Vision Transformers for Single Image Dehazing[J].arXiv:2204.03883,2022. [12]LIU Z,LIN Y T,CAO Y,et al.Swin transformer:Hierarchical vision transformer using shifted windows[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2021:10012-10022. [13]CAI B L,XU X M,JIA K,et al.Dehazenet:An end-to-end system for single image haze removal[J].IEEE Transactions on Image Processing,2016,25(11):5187-5198. [14]REN W Q,LIU S,ZHANG H,et al.Single image dehazing via multi-scale convolutional neural networks[C]//Proceedings of the European Conference on Computer Vision.Springer,2016:154-169. [15]WU H Y,QU Y Y,LIN S H,et al.Contrastive Learning for Compact Single Image Dehazing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:10551-10560. [16]YU H,ZHENG N S,ZHOU M,et al.Frequency and SpatialDual Guidance for Image Dehazing[C]//Proceedings of the European Conference on Computer Vision.Springer,2022:181-198. [17]SHAO Y J,LI L R H,REN W Q,et al.Domain Adaptation for Image Dehazing[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:2805-2814. [18]LI B Y,GOU Y B,GU S H,et al.You Only Look Yourself:Unsupervised and Untrained Single Image Dehazing Neural Network [J].International Journal of Computer Vision,2021,129(5):1754-1767. [19]CHEN H T,WANG Y H,GUO T Y,et al.Pre-Trained Image Processing Transformer[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:12299-12310. [20]LIANG J Y,CAO J Z,SUN G L,et al.SwinIR:Image Restoration Using Swin Transformer[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision Workshop.2021:1833-1844. [21]LI X,JIN X,YU T,et al.Learning Omni-Frequency Region-adaptive Representations for Real Image Super-Resolution[C]//Proceedings of the AAAI Conference on Artificial Intelligence.2021:1975-1983. [22]LI X,WANG W H,HU X L,et al.Selective Kernel Networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2019:510-519. [23]WANG X T,KELVIN C K C,YU K,et al.EDVR:Video Restoration With Enhanced Deformable Convolutional Networks[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops.2019:1954-1963. [24]LIU Y,PAN J S,REN J,et al.Learning Deep Priors for Image Dehazing[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision.2019:2492-2500. [25]DONG H,PAN J S,XIANG L,et al.Multi Scale Boosted Deha-zing Network With Dense Feature Fusion[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:2154-2164. [26]HONG M,XIE Y,LI C H,et al.Distilling Image Dehazing With Heterogeneous Task Imitation[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2020:3459-3468. [27]CHEN Z Y,WANG Y C,YANG Y,et al.PSD:Principled Synthetic-to-Real Dehazing Guided by Physical Priors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:7180-7189. [28]ZHANG R,ISOLA P,EFROS A A,et al.The Unreasonable Effectiveness of Deep Features as a Perceptual Metric[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2018:586-595. [29]MITTAL A,SOUNDARARAJAN R,BOVIK A C.Making a“Completely Blind” Image Quality Analyzer[J].IEEE Signal Processing Letters,2013,20(3):209-212. [30]LI B Y,REN W Q,FU D P,et al.Benchmarking Single-Image Dehazing and Beyond [J].IEEE Transactions on Image Proces-sing,2010,28(1):492-505. [31]YIN W,ZHANG J M,WANG O,et al.Learning To Recover 3D Scene Shape From a Single Image[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition.2021:204-213. |
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