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 ea...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.展开更多
文摘针对工业场景下图像模糊、分辨率低、边缘细节不明显等问题,提出一种基于生成对抗网络的低质图像增强算法。首先,设计退化网络获得与真实场景更为接近的低质图像,以此与现实高清图像获得特征映射关系;其次,在使用密集残差块(residual in residual dense block,RRDB)的基础上添加卷积注意力模块,增强RRDB网络的特征表达能力,以有效地捕获关键特征信息;最后,设计边缘增强网络模块结合改进的RRDB作为生成器,图像细节信息的捕捉与还原能力得到显著提升,并与判别器对抗生成更高质量的图像。实验结果表明,相较于现有常用的图像增强算法,所提算法能有效提升工业场景图像清晰度、保留图像细节并减少失真。定量指标峰值信噪比平均提升10.45%,结构相似性平均提升15.92%,运行速度快,能满足工业生产需求。
文摘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.
