Underwater Image Enhancement Based on Multi-scale Adversarial Network

In this study,an underwater image enhancement method based on multi-scale adversarial network was proposed to solve the problem of detail blur and color distortion in underwater images.Firstly,the local features of each layer were enhanced into the global features by the proposed residual dense block,which ensured that the generated images retain more details.Secondly,a multi-scale structure was adopted to extract multi-scale semantic features of the original images.Finally,the features obtained from the dual channels were fused by an adaptive fusion module to further optimize the features.The discriminant network adopted the structure of the Markov discriminator.In addition,by constructing mean square error,structural similarity,and perceived color loss function,the generated image is consistent with the reference image in structure,color,and content.The experimental results showed that the enhanced underwater image deblurring effect of the proposed algorithm was good and the problem of underwater image color bias was effectively improved.In both subjective and objective evaluation indexes,the experimental results of the proposed algorithm are better than those of the comparison algorithm.

基于量测数据补全的有源配电网电压优化技术

有源配电网由于分布式新能源影响,电压存在越限的风险,但配电网实时量测仅可部分观测,优化问题无法求解。针对这个问题,提出了一种基于量测数据补全的有源配电网电压优化技术。在仅能获得部分节点实时量测的状态下,采用增强生成对抗网络补全算法,得到完整的配电网量测数据。根据补全的实时数据,计及补全误差,设计电压误差修正模型,修正优化电压目标,在电压越限时对配电网电压进行优化,提高电压质量。通过IEEE 33节点算例验证了所提方法相对于生成对抗网络在部分实时观测的情况下能够高精度补全缺失量测数据,降低电压波动,提高配电网运行的稳定性。