Laser & Optoelectronics Progress, Volume. 62, Issue 15, 1500009(2025)
Research Advances on Unsupervised Networks-Driven Imaging Through Scattering Media (Invited)
Fig. 2. Algorithms for AE-based IUL methods. (a1)(a2) Network structure of underwater image and video enhancement framework based on convolutional AE and its comparative results with different methods[51]; (b1)(b2) network structure of Retinex algorithm enhanced framework based on fractional integration and comparison results with different methods[52]; (c) network structure of UDNet enhancement framework[53]; (d) network structure of the RQUNet-VAE method[54]
Fig. 3. Different algorithms for GAN-based IUL methods. (a1)(a2) Network structure of unsupervised underwater visual enhancement methods and the results of underwater visual enhancement produced by their outputs[62]; (b1)(b2) network structure of low-light visual enhancement methods and comparative experimental results of different methods[63]; (c) network structure of MAGAN[64]; (d) the method of attention-guided dual-discriminator based GAN[65]
Fig. 5. IUL methods based on different CycleGAN. (a1)(a2) CycleGAN-based target recovery method and model reconstruction results[67]; (b1)(b2) network structure combining cyclic consistency loss and perceptual loss and its comparison with CycleGAN[68]; (c) network structure of Cycle-SNSPGAN method[69]; (d) network structure of the UME-Net method[70]
Fig. 6. GAN based on physical models and other learning approaches. (a1)(a2) Network structure of the priori-free GAN incorporating autocorrelation consistency and model output results[71]; (b1)(b2) network structure of PDR-GAN and comparison results with other methods[72]; (c) network structure of EnlightenGAN[73]; (d1)(d2) flow of UCL-Dehaze and comparison results with other methods[74]; (e) PatchNCE network structure based on contrast learning [75]
Fig. 8. DDPM-based IUL methods. (a1)(a2) Enhanced network model and its output results [79]; (b) new biconditional-based DDPM [80]; (c1)(c2) DDPM-based fusion method and comparison results of different road inspection methods [81]; (d1)(d2) hybrid model based on DDPM and atmospheric scattering model and results of the model dehazing and detection [82]
Fig. 9. CNN-based IUL methods. (a1)(a2) Network structure of dehazing algorithm based on interactive fusion module and iterative optimization module[84]; (b1)(b2) algorithm process of similarity fusion strategy and its comparison results with different methods[85]; (c1)(c2) network structure of unsupervised fusion method and its comparison results with different methods[86]; (d1)(d2) network structure of generating pseudo labels through bright channels and its comparison results with different methods[87]; (e1)(e2) specific algorithm process of synthesizing pseudo real images through multi-source images and its comparison results with different methods[89]; (f1)(f2) specific algorithm process for inferring the distribution of real data through estimating noise models and its comparison results with other methods[90]
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Longyu Qiao, Bing Lin, Xueqiang Fan, Xixun Sun, Zhiyong Peng, Zhongyi Guo. Research Advances on Unsupervised Networks-Driven Imaging Through Scattering Media (Invited)[J]. Laser & Optoelectronics Progress, 2025, 62(15): 1500009
Category: Reviews
Received: Apr. 9, 2025
Accepted: May. 13, 2025
Published Online: Aug. 6, 2025
The Author Email: Zhongyi Guo (guozhongyi@hfut.edu.cn)
CSTR:32186.14.LOP250975