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
Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisyrepresentation. In this paper,...Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisyrepresentation. In this paper, a recognition method, involving a novel visual attention mechanismbased Gabor region proposal sub-network(Gabor RPN) and improved refinement generative adversarial sub-network(GAN), is proposed. Novel central-peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset(GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect.展开更多
The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time...The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.展开更多
由于低照度配对图像的制作成本昂贵且难于制作,而非配对低照度图像增强方法不依赖配对图像数据因而更有实用价值,但其缺乏详细的监督信号导致输出图像存在全局曝光不一致、色彩失真和大量噪声等视觉退化问题,在实际应用中存在挑战.为了...由于低照度配对图像的制作成本昂贵且难于制作,而非配对低照度图像增强方法不依赖配对图像数据因而更有实用价值,但其缺乏详细的监督信号导致输出图像存在全局曝光不一致、色彩失真和大量噪声等视觉退化问题,在实际应用中存在挑战.为了更好地满足实用需求,提出一种基于全局一致的非配对低照度增强方法(unpaired low-light enhancement method based on global consistency,GCLLE).首先,该方法通过全局一致性保持模块(global consistency preserving module,GCPM)将编码器和解码器中相同尺度的特征重新建模并融合以矫正不同尺度的上下文信息,保证输出图像全局曝光调整一致性和全局结构一致性,使得图像亮度分布均匀并避免扭曲和失真;利用局部平滑和调制模块(local smoothing and modulation module,LSMM)学习一组局部的低阶曲线映射,为图像提供更宽的动态范围并进一步提高质量,实现真实和自然的增强效果;提出使用双路池化融合深层特征的深度特征强化模块(deep feature enhancement module,DFEM)压缩无关信息并突出更有区分度的编码特征,减少了不准确信息并使得解码器更容易捕获图像中的低强度信号,保留图像更多细节.不同于关注配对图像像素间一对一映射关系的配对增强方法,GCLLE通过缩小低照度图像与非配对正常照度图像之间的风格差异实现增强.在MIT和LSRW数据集上进行大量的实验,结果表明所提方法在多个客观指标上超过了现有典型低照度增强方法,具有更好的增强效果.展开更多
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.
基金the National Key Research and Development Program of China(No.2016YFC0802904)National Natural Science Foundation of China(No.61671470)Natural Science Foundation of Jiangsu Province(BK20161470).
文摘Ground military target recognition plays a crucial role in unmanned equipment and grasping the battlefield dynamics for military applications, but is disturbed by low-resolution and noisyrepresentation. In this paper, a recognition method, involving a novel visual attention mechanismbased Gabor region proposal sub-network(Gabor RPN) and improved refinement generative adversarial sub-network(GAN), is proposed. Novel central-peripheral rivalry 3D color Gabor filters are proposed to simulate retinal structures and taken as feature extraction convolutional kernels in low-level layer to improve the recognition accuracy and framework training efficiency in Gabor RPN. Improved refinement GAN is used to solve the problem of blurry target classification, involving a generator to directly generate large high-resolution images from small blurry ones and a discriminator to distinguish not only real images vs. fake images but also the class of targets. A special recognition dataset for ground military target, named Ground Military Target Dataset(GMTD), is constructed. Experiments performed on the GMTD dataset effectively demonstrate that our method can achieve better energy-saving and recognition results when low-resolution and noisy-representation targets are involved, thus ensuring this algorithm a good engineering application prospect.
基金supported in part by the National Natural Science Foundation of China(Grant No.62276274)Shaanxi Natural Science Foundation(Grant No.2023-JC-YB-528)Chinese aeronautical establishment(Grant No.201851U8012)。
文摘The automatic stealth task of military time-sensitive targets plays a crucial role in maintaining national military security and mastering battlefield dynamics in military applications.We propose a novel Military Time-sensitive Targets Stealth Network via Real-time Mask Generation(MTTSNet).According to our knowledge,this is the first technology to automatically remove military targets in real-time from videos.The critical steps of MTTSNet are as follows:First,we designed a real-time mask generation network based on the encoder-decoder framework,combined with the domain expansion structure,to effectively extract mask images.Specifically,the ASPP structure in the encoder could achieve advanced semantic feature fusion.The decoder stacked high-dimensional information with low-dimensional information to obtain an effective mask layer.Subsequently,the domain expansion module guided the adaptive expansion of mask images.Second,a context adversarial generation network based on gated convolution was constructed to achieve background restoration of mask positions in the original image.In addition,our method worked in an end-to-end manner.A particular semantic segmentation dataset for military time-sensitive targets has been constructed,called the Military Time-sensitive Target Masking Dataset(MTMD).The MTMD dataset experiment successfully demonstrated that this method could create a mask that completely occludes the target and that the target could be hidden in real time using this mask.We demonstrated the concealment performance of our proposed method by comparing it to a number of well-known and highly optimized baselines.
文摘由于低照度配对图像的制作成本昂贵且难于制作,而非配对低照度图像增强方法不依赖配对图像数据因而更有实用价值,但其缺乏详细的监督信号导致输出图像存在全局曝光不一致、色彩失真和大量噪声等视觉退化问题,在实际应用中存在挑战.为了更好地满足实用需求,提出一种基于全局一致的非配对低照度增强方法(unpaired low-light enhancement method based on global consistency,GCLLE).首先,该方法通过全局一致性保持模块(global consistency preserving module,GCPM)将编码器和解码器中相同尺度的特征重新建模并融合以矫正不同尺度的上下文信息,保证输出图像全局曝光调整一致性和全局结构一致性,使得图像亮度分布均匀并避免扭曲和失真;利用局部平滑和调制模块(local smoothing and modulation module,LSMM)学习一组局部的低阶曲线映射,为图像提供更宽的动态范围并进一步提高质量,实现真实和自然的增强效果;提出使用双路池化融合深层特征的深度特征强化模块(deep feature enhancement module,DFEM)压缩无关信息并突出更有区分度的编码特征,减少了不准确信息并使得解码器更容易捕获图像中的低强度信号,保留图像更多细节.不同于关注配对图像像素间一对一映射关系的配对增强方法,GCLLE通过缩小低照度图像与非配对正常照度图像之间的风格差异实现增强.在MIT和LSRW数据集上进行大量的实验,结果表明所提方法在多个客观指标上超过了现有典型低照度增强方法,具有更好的增强效果.
