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Turbulent-image Restoration Based on a Compound Multibranch Feature Fusion Network
Banglian Xu,Yao Fang,Leihong Zhang,Dawei Zhang,Lulu Zheng Optical Society of Korea 2023 Current Optics and Photonics Vol.7 No.3
In middle- and long-distance imaging systems, due to the atmospheric turbulence caused by temperature, wind speed, humidity, and so on, light waves propagating in the air are distorted, resulting in image-quality degradation such as geometric deformation and fuzziness. In remote sensing, astronomical observation, and traffic monitoring, image information loss due to degradation causes huge losses, so effective restoration of degraded images is very important. To restore images degraded by atmospheric turbulence, an image-restoration method based on improved compound multibranch feature fusion (CMFNetPro) was proposed. Based on the CMFNet network, an efficient channel-attention mechanism was used to replace the channel-attention mechanism to improve image quality and network efficiency. In the experiment, two-dimensional random distortion vector fields were used to construct two turbulent datasets with different degrees of distortion, based on the Google Landmarks Dataset v2 dataset. The experimental results showed that compared to the CMFNet, DeblurGAN-v2, and MIMO-UNet models, the proposed CMFNetPro network achieves better performance in both quality and training cost of turbulent-image restoration. In the mixed training, CMFNetPro was 1.2391 dB (weak turbulence), 0.8602 dB (strong turbulence) respectively higher in terms of peak signal-to-noise ratio and 0.0015 (weak turbulence), 0.0136 (strong turbulence) respectively higher in terms of structure similarity compared to CMFNet. CMFNetPro was 14.4 hours faster compared to the CMFNet. This provides a feasible scheme for turbulent-image restoration based on deep learning.
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Yidi Wang,Bowen Li,Guanshu Li,Yingying Huang,Dawei Fang,Jun Wang,Youtao Song 한국공업화학회 2017 Journal of Industrial and Engineering Chemistry Vol.47 No.-
Three crystal phases of BiPO4 (HBIP, nMBIP and mMBIP), corresponding photocatalysts (Er3+:Y3Al5O12/HBIP, Er3+:Y3Al5O12/nMBIP and Er3+:Y3Al5O12/mMBIP) and their nanocomposite photocatalyst (Er3+:Y3Al5O12/(H-nM-mM)BIP) were prepared by hydrothermal, ultrasonic dispersion and liquid boilingmethods. The prepared photocatalysts were characterized by X-ray diffractometer (XRD), energydispersive X-ray spectroscopy (EDX), scanning electron microscopy (SEM) and UV–vis diffuse reflectancespectra (DRS). The catalytic activity of prepared photocatalysts was evaluated via photocatalyticconversion rate of nitrite under ultraviolet-light, visible-light and simulated solar-light irradiations. These three photocatalysts all exhibited excellent performance under simulated solar-light irradiationand reached 85.36%, 84.42% and 78.53% conversion rates, respectively, for Er3+:Y3Al5O12/mMBIP, Er3+:Y3Al5O12/nMBIP and Er3+:Y3Al5O12/HBIP. Particularly, Er3+:Y3Al5O12/(H-nM-mM)BIP was also found toplay a high catalytic activity, resulting in 78.68%, 70.04% and 82.57% conversion rates, respectively, underultraviolet-light, visible-light and simulated solar-light irradiation. In addition, the study of used timesshowed that the prepared photocatalysts can be effectively recycled without an apparent inactivation onthe photocatalytic activity. This research may provide a potential way for converting nitrite and othercontaminants by utilizing solar energy efficiently.
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