【目的】高光谱图像因其丰富的光谱信息而备受关注,然而,由于成像硬件条件的限制,通常很难直接获得高空间分辨率的高光谱图像。为了提高分辨率,将高光谱图像与从同一场景采集的高空间分辨率的多光谱图像融合是一种经济有效的方法。然而...【目的】高光谱图像因其丰富的光谱信息而备受关注,然而,由于成像硬件条件的限制,通常很难直接获得高空间分辨率的高光谱图像。为了提高分辨率,将高光谱图像与从同一场景采集的高空间分辨率的多光谱图像融合是一种经济有效的方法。然而,现有的大多数基于深度学习的方法未充分挖掘图像间空间和光谱相关性,导致融合性能受限。【方法】本文提出了一种结合图像去噪、光谱特征与空间特征增强的高光谱图像超分辨率融合方法。首先,通过使用不同标准差的高斯模糊核对高光谱与多光谱图像进行高斯模糊处理,有效减少这2种模态图像中包含的噪声。其次,为了提高融合图像的精确度,在利用不同模态图像间光谱和空间相关性重建高分辨率图像时,分别引入通道注意力和空间注意力,利用增强图像关键信息的方式获得不同模态间更好的空间和光谱相关性。最后,利用增强的空间和光谱相关性,将映射得到的高分辨率图像特征聚合起来,重建出高空间分辨率的高光谱图像。【结果】在ZY-m和Chikusei数据集上融合结果的PSNR分别为53.586和53.738,在ZY-m数据集上较次优方法空谱解耦互引导网络(Spatial-Spectral Unfolding Network with Mutual Guidance,SMGU-Net)提高2.8%,在Chikusei数据集上较次优方法带有双条件调制模块的扩散模型(Diffusion Model with two Conditional Modulation Modules,DDIF)提高1.70%;SAM值达到0.006和0.018,在ZY-m数据集上较次优方法 SMGU-Net降低14.28%,在Chikusei数据集上较次优方法 DDIF降低5.26%。【结论】本文方法具有良好的光谱保真度和空间细节增强能力,为高光谱图像的超分辨率提供了一种有效技术方案,展示了其在国土资源勘查、环境监测等领域的良好应用潜力。展开更多
Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with co...Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with conventional methods such as fundus photography,optical coherence tomography(OCT),and slit-lamp imaging,much depends on an expert’s interpretation of the images,making the systems very labor-intensive to operate.Moreover,clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices.To solve these problems,we have developed the Efficient Channel-Spatial Attention Network(ECSA-Net),a new deep learning-based methodology that integrates lightweight channel-and spatial-attention modules into a convolutional neural network.Ultimately,ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images.The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network(CNN)architectures.The results showed that the ECSA-Net achieved classification accuracies of 60.00%and 69.92%,respectively,while using only a compact architecture with 0.56 million parameters.This represents a reduction in parameter size by a factor of 14×to 247×compared to other pretrained models.Additionally,the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost,making ECSA-Net a viable option for real-time clinical use.ECSA-Net is both efficient and accurate in automating the classification of eye diseases,combining high performance with the ethical considerations of medical artificial intelligence(AI)deployment.The ECSA-Net frameworkmitigates algorithmic bias in training datasets and protects individuals’privacy and transparency in decision-making,thereby facilitating human-AI collaboration.The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings.展开更多
针对UAV在仿真实验中自动跟踪移动目标的需求,提出基于改进CSRT(channel and spatial reliability-aware tracker)算法的无人机长时自动跟踪方法。通过导向滤波加拉普拉斯算子LOGF(laplacian of guided filter)检测获取目标边缘特征,再...针对UAV在仿真实验中自动跟踪移动目标的需求,提出基于改进CSRT(channel and spatial reliability-aware tracker)算法的无人机长时自动跟踪方法。通过导向滤波加拉普拉斯算子LOGF(laplacian of guided filter)检测获取目标边缘特征,再与HOG(histogram of oriented gradient)和CN(color names)特征融合,增强算法对目标的判别能力;使用平均峰值相关能量和感知哈希汉明距离来综合判定目标状态,当判定目标被遮挡时,采用YOLOv8定位目标,再将定位结果传输至跟踪算法继续跟踪目标。仿真结果表明:在搭建的仿真环境中算法能够在目标被遮挡时仍能长时稳定的跟踪目标,为无人机目标跟踪算法研究提供了良好的仿真实验环境。展开更多
文摘【目的】高光谱图像因其丰富的光谱信息而备受关注,然而,由于成像硬件条件的限制,通常很难直接获得高空间分辨率的高光谱图像。为了提高分辨率,将高光谱图像与从同一场景采集的高空间分辨率的多光谱图像融合是一种经济有效的方法。然而,现有的大多数基于深度学习的方法未充分挖掘图像间空间和光谱相关性,导致融合性能受限。【方法】本文提出了一种结合图像去噪、光谱特征与空间特征增强的高光谱图像超分辨率融合方法。首先,通过使用不同标准差的高斯模糊核对高光谱与多光谱图像进行高斯模糊处理,有效减少这2种模态图像中包含的噪声。其次,为了提高融合图像的精确度,在利用不同模态图像间光谱和空间相关性重建高分辨率图像时,分别引入通道注意力和空间注意力,利用增强图像关键信息的方式获得不同模态间更好的空间和光谱相关性。最后,利用增强的空间和光谱相关性,将映射得到的高分辨率图像特征聚合起来,重建出高空间分辨率的高光谱图像。【结果】在ZY-m和Chikusei数据集上融合结果的PSNR分别为53.586和53.738,在ZY-m数据集上较次优方法空谱解耦互引导网络(Spatial-Spectral Unfolding Network with Mutual Guidance,SMGU-Net)提高2.8%,在Chikusei数据集上较次优方法带有双条件调制模块的扩散模型(Diffusion Model with two Conditional Modulation Modules,DDIF)提高1.70%;SAM值达到0.006和0.018,在ZY-m数据集上较次优方法 SMGU-Net降低14.28%,在Chikusei数据集上较次优方法 DDIF降低5.26%。【结论】本文方法具有良好的光谱保真度和空间细节增强能力,为高光谱图像的超分辨率提供了一种有效技术方案,展示了其在国土资源勘查、环境监测等领域的良好应用潜力。
基金Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2026R77)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia,the Deanship of Scientific Research at Northern Border University,Arar,Saudi Arabia,through the project number NBU-FFR-2026-2248-01.
文摘Globally,diabetes and glaucoma account for a high number of people suffering from severe vision loss and blindness.To treat these vision disorders effectively,proper diagnosis must occur in a timely manner,and with conventional methods such as fundus photography,optical coherence tomography(OCT),and slit-lamp imaging,much depends on an expert’s interpretation of the images,making the systems very labor-intensive to operate.Moreover,clinical settings face difficulties with inter-observer variability and limited scalability with these diagnostic devices.To solve these problems,we have developed the Efficient Channel-Spatial Attention Network(ECSA-Net),a new deep learning-based methodology that integrates lightweight channel-and spatial-attention modules into a convolutional neural network.Ultimately,ECSA-Net improves the efficiency of computational resource use while enhancing discriminative feature extraction from retinal images.The ECSA-Net methodology was validated by conducting a series of classification accuracy tests using two publicly available eye disease datasets and was benchmark against a number of different pretrained convolutional neural network(CNN)architectures.The results showed that the ECSA-Net achieved classification accuracies of 60.00%and 69.92%,respectively,while using only a compact architecture with 0.56 million parameters.This represents a reduction in parameter size by a factor of 14×to 247×compared to other pretrained models.Additionally,the attention modules added to the architecture significantly increased sensitivity to disease-relevant regions of the retina while maintaining low computational cost,making ECSA-Net a viable option for real-time clinical use.ECSA-Net is both efficient and accurate in automating the classification of eye diseases,combining high performance with the ethical considerations of medical artificial intelligence(AI)deployment.The ECSA-Net frameworkmitigates algorithmic bias in training datasets and protects individuals’privacy and transparency in decision-making,thereby facilitating human-AI collaboration.The two areas of technical performance and ethical integration are needed for the responsible and scalable use of ECSA-Net in a variety of ophthalmic care settings.
文摘针对UAV在仿真实验中自动跟踪移动目标的需求,提出基于改进CSRT(channel and spatial reliability-aware tracker)算法的无人机长时自动跟踪方法。通过导向滤波加拉普拉斯算子LOGF(laplacian of guided filter)检测获取目标边缘特征,再与HOG(histogram of oriented gradient)和CN(color names)特征融合,增强算法对目标的判别能力;使用平均峰值相关能量和感知哈希汉明距离来综合判定目标状态,当判定目标被遮挡时,采用YOLOv8定位目标,再将定位结果传输至跟踪算法继续跟踪目标。仿真结果表明:在搭建的仿真环境中算法能够在目标被遮挡时仍能长时稳定的跟踪目标,为无人机目标跟踪算法研究提供了良好的仿真实验环境。
