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学者姓名：沈英
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Abstract ：
Attention mechanisms have been introduced to exploit deep-level information for image restoration by capturing feature dependencies. However, existing attention mechanisms often have limited perceptual capabilities and are incompatible with low-power devices due to computational resource constraints. Therefore, we propose a feature enhanced cascading attention network (FECAN) that introduces a novel feature enhanced cascading attention (FECA) mechanism, consisting of enhanced shuffle attention (ESA) and multi-scale large separable kernel attention (MLSKA). Specifically, ESA enhances high-frequency texture features in the feature maps, and MLSKA executes the further extraction. The rich and fine-grained high-frequency information are extracted and fused from multiple perceptual layers, thus improving super-resolution (SR) performance. To validate FECAN's effectiveness, we evaluate it with different complexities by stacking different numbers of high-frequency enhancement modules (HFEM) that contain FECA. Extensive experiments on benchmark datasets demonstrate that FECAN outperforms state-of-the-art lightweight SR networks in terms of objective evaluation metrics and subjective visual quality. Specifically, at a x 4 scale with a 121 K model size, compared to the second-ranked MAN-tiny, FECAN achieves a 0.07 dB improvement in average peak signal-to-noise ratio (PSNR), while reducing network parameters by approximately 19% and FLOPs by 20%. This demonstrates a better trade-off between SR performance and model complexity.
Keyword ：
Convolution neural network Convolution neural network Enhanced shuffle attention Enhanced shuffle attention Lightweight image super-resolution Lightweight image super-resolution Multi-scale large separable kernel attention Multi-scale large separable kernel attention
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| GB/T 7714 | Huang, Feng , Liu, Hongwei , Chen, Liqiong et al. Feature enhanced cascading attention network for lightweight image super-resolution [J]. | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
| MLA | Huang, Feng et al. "Feature enhanced cascading attention network for lightweight image super-resolution" . | SCIENTIFIC REPORTS 15 . 1 (2025) . |
| APA | Huang, Feng , Liu, Hongwei , Chen, Liqiong , Shen, Ying , Yu, Min . Feature enhanced cascading attention network for lightweight image super-resolution . | SCIENTIFIC REPORTS , 2025 , 15 (1) . |
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针对现有图像超分辨率重建方法存在模型复杂度过高和参数量过大等问题,文中提出基于多尺度空间自适应注意力网络(Multi-scale Spatial Adaptive Attention Network,MSAAN)的轻量级图像超分辨率重建方法.首先,设计全局特征调制模块(Global Feature Modulation Module,GFM),学习全局纹理特征.同时,设计轻量级的多尺度特征聚合模块(Multi-scale Feature Aggregation Module,MFA),自适应聚合局部至全局的高频空间特征.然后,融合GFM和MFA,提出多尺度空间自适应注意力模块(Multi-scale Spatial Adaptive Attention Module,MSAA).最后,通过特征交互门控前馈模块(Feature Interactive Gated Feed-Forward Module,FIGFF)增强局部信息提取能力,同时减少通道冗余.大量实验表明,MSAAN能捕捉更全面、更精细的特征,在保证轻量化的同时显著提升图像的重建效果.
Keyword ：
Transformer Transformer 卷积神经网络 卷积神经网络 多尺度空间自适应注意力 多尺度空间自适应注意力 轻量级图像超分辨率重建 轻量级图像超分辨率重建
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| GB/T 7714 | 黄峰 , 刘鸿伟 , 沈英 et al. 基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法 [J]. | 模式识别与人工智能 , 2025 , 38 (1) : 36-50 . |
| MLA | 黄峰 et al. "基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法" . | 模式识别与人工智能 38 . 1 (2025) : 36-50 . |
| APA | 黄峰 , 刘鸿伟 , 沈英 , 裘兆炳 , 陈丽琼 . 基于多尺度空间自适应注意力网络的轻量级图像超分辨率方法 . | 模式识别与人工智能 , 2025 , 38 (1) , 36-50 . |
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叶黄素是天然的抗氧化剂,对人体健康有多种益处,异养小球藻具有叶黄素纯度和产量均较高的优势,而小球藻叶黄素产量主要取决于生物质产量和叶黄素含量两个因素.传统的光密度法测生物质产量和高效液相色谱法测叶黄素含量存在操作复杂、时效性低等不足.为了快速、无损测定小球藻生长过程中叶黄素含量变化,搭建可见-近红外双模式快照式多光谱成像检测系统,根据光谱响应区域,分别利用可见光相机获取叶黄素光谱信息,近红外相机获取生物质光谱信息,构建含有生物质量和叶黄素含量信息的可见-近红外双模式多光谱数据集.针对系统所使用的快照式多光谱相机光谱范围宽、波长数量少的特征波长选取问题,提出一种结合序列浮动前向选择的改进型连续投影算法(mSPA);将mSPA与常规的连续投影算法、遗传算法及随机蛙跳三种波长选择算法作对比分析后,构建了基于特征波长的多元线性回归和极限学习机模型;最后,利用生物质产量和叶黄素含量的最佳预测模型生成小球藻叶黄素产量的可视化分布图.结果表明,在利用近红外、可见光相机分别检测小球藻生物质、叶黄素量时,mSPA得到的特征波长数均较少,并具有最高的预测精度.生物质量与叶黄素含量的最佳模型均为mSPA筛选特征波长后建立的极限学习机模型,对应的预测集决定系数分别为0.947和0.907,预测集均方根误差分别为0.698 g·L-1和0.077 mg·g-1,剩余预测偏差分别为3.535和3.338,模型的预测能力较好.可视化分布实现了直观监测小球藻叶黄素产量的变化,有助于后续实际生产中在线检测叶黄素产量.mSPA在快照式多光谱检测小球藻生物质含量及叶黄素含量中,通过对排序波长逐个评估以选择出最佳特征波长组合,有效地避免了特征波长的错选、漏选,提高了模型的预测精度,为快照式多光谱成像技术应用提供新的波长选择思路.
Keyword ：
叶黄素产量 叶黄素产量 小球藻 小球藻 快照式多光谱 快照式多光谱 特征波长 特征波长
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| GB/T 7714 | 沈英 , 占秀兴 , 黄春红 et al. 基于快照式多光谱特征波长的小球藻叶黄素产量快速测定 [J]. | 光谱学与光谱分析 , 2024 , 44 (8) : 2216-2223 . |
| MLA | 沈英 et al. "基于快照式多光谱特征波长的小球藻叶黄素产量快速测定" . | 光谱学与光谱分析 44 . 8 (2024) : 2216-2223 . |
| APA | 沈英 , 占秀兴 , 黄春红 , 谢友坪 , 郭翠霞 , 黄峰 . 基于快照式多光谱特征波长的小球藻叶黄素产量快速测定 . | 光谱学与光谱分析 , 2024 , 44 (8) , 2216-2223 . |
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为了提高车辆视觉感知能力,针对交通场景运用提出一种改进的轮廓角方向(contour angle orientation,CAO)算法用于实现红外与可见光图像配准.通过模拟不同的交通场景,对成熟算法进行性能检测对比,选出CAO算法这一优势算法,并对其粗匹配参数和图像预处理图像缩放程序做了改进.实验表明,改进后的CAO算法细匹配更精准,马赛克拼接图拼接处衔接更加自然,线条更加顺滑,效果更好.与原来CAO算法相比,改进后的算法均方根误差值RMSE下降 3.29%,查准率Precision提高 2.13%,平均运算耗时减少 0.11 s,在配准精度和配准实时性方面均证明了算法的改进效果.
Keyword ：
图像配准 图像配准 特征提取 特征提取 红外和可见光图像 红外和可见光图像 轮廓角方向 轮廓角方向
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| GB/T 7714 | 苏建彬 , 沈英 , 黄磊 et al. 车载红外和可见光图像配准方法 [J]. | 红外技术 , 2024 , 46 (10) : 1209-1217 . |
| MLA | 苏建彬 et al. "车载红外和可见光图像配准方法" . | 红外技术 46 . 10 (2024) : 1209-1217 . |
| APA | 苏建彬 , 沈英 , 黄磊 , 沈元兴 . 车载红外和可见光图像配准方法 . | 红外技术 , 2024 , 46 (10) , 1209-1217 . |
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红外与可见光图像因其显著的光谱特性差异,在配准过程中易出现特征点误匹配率高的问题.当前广泛应用的误匹配剔除算法通常采用随机采样结合模型拟合的策略,这类方法往往难以兼顾配准精度和速度,表现为算法迭代次数过高或鲁棒性不强.针对这一问题,提出一种基于空间约束的优先采样一致性(SC-PRISAC)误匹配剔除算法.利用材料辐射率差异设计兼具红外与可见光特征的双光谱标定靶标,基于双边滤波金字塔标定获取相机内外参数,在此基础上利用极线约束定理和深度一致性原则构建异源图像间的空间约束关系.使用高质量特征点优先采样策略减少了算法的迭代次数,有效剔除误匹配特征点.实验表明:所提算法实现了亚像素红外与可见光双目标定,标定误差降低至0.430 pixel;在提高配准精度的同时,也有效提升了处理速度,单应性矩阵估计误差为7.857,处理时间仅为1.919 ms,各项性能均优于RANSAC(random sample consensus)等算法.所提算法为红外与可见光图像配准提供一种更为可靠和高效的误匹配剔除解决方案.
Keyword ：
双目标定 双目标定 图像配准 图像配准 极线约束 极线约束 误匹配特征点剔除 误匹配特征点剔除
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| GB/T 7714 | 沈英 , 林烨 , 陈海涛 et al. 空间约束下异源图像误匹配特征点剔除算法 [J]. | 光学学报 , 2024 , 44 (20) : 208-219 . |
| MLA | 沈英 et al. "空间约束下异源图像误匹配特征点剔除算法" . | 光学学报 44 . 20 (2024) : 208-219 . |
| APA | 沈英 , 林烨 , 陈海涛 , 吴靖 , 黄峰 . 空间约束下异源图像误匹配特征点剔除算法 . | 光学学报 , 2024 , 44 (20) , 208-219 . |
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Polarization imaging technology can be applied to unveil the interaction between light and matter by harnessing the transverse vector wave attributes of light, thus to accentuate target characteristics amidst complex weather conditions. This technology has the potential to be widely used in road target detection. However, polarization detection is significantly affected by illumination and detection angles, as well as the considerable variation in the scale of road targets. The optimal polarization parameters should be adaptively adjusted to weather conditions, angles and target features, whereas most existing research employs handcrafted polarization parameters without considering actual complex detection requirements, which are unable to adaptively adjust the polarization feature enhancement methods. In this paper, we propose a road target detection algorithm based on an end-to-end adaptive polarization coding method, named YOLO-Polarization of Road Target Detection (YOLO-PRTD). To enhance the polarized features of targets under complex weather conditions, an Adaptive Polarization Coding Module (APCM) is designed. This module integrates channel-wise global self-attention and small kernel convolution to adaptively adjust the polarization enhancement method using dynamically extracted global and local polarization feature information. A multi-scale detection network is also designed to fully extract and fuse multi-scale feature information from receptive fields, channels, and spaces in different dimensions. Additionally, a dataset of Polarized Images of Road Targets in Complex Weather conditions (PIRT-CW) is proposed for training and evaluation. Experimental results on the PIRT-CW show that the YOLO-PRTD algorithm achieves a mAP0.5 of 89.83%, reducing the error rate by 15.54% compared to the baseline network YOLOX.
Keyword ：
Complex weather conditions Complex weather conditions Multi-scale Multi-scale Polarization feature enhancement Polarization feature enhancement Road target detection Road target detection
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| GB/T 7714 | Huang, Feng , Zheng, Junlong , Liu, Xiancai et al. Polarization of road target detection under complex weather conditions [J]. | SCIENTIFIC REPORTS , 2024 , 14 (1) . |
| MLA | Huang, Feng et al. "Polarization of road target detection under complex weather conditions" . | SCIENTIFIC REPORTS 14 . 1 (2024) . |
| APA | Huang, Feng , Zheng, Junlong , Liu, Xiancai , Shen, Ying , Chen, Jinsheng . Polarization of road target detection under complex weather conditions . | SCIENTIFIC REPORTS , 2024 , 14 (1) . |
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偏振可以提高无人机的自主侦察能力，但易受到探测角度和目标材质的影响，从而降低偏振检测的鲁棒性。为此，提出一种基于偏振图像的低空伪装目标实时检测算法YOLO-P,采用融合多偏振方向信息的编码图像作为输入，应用三维卷积模块提取不同偏振方向图像之间的联系特征；引入特征增强模块对多层次特征进行进一步增强；采用跨层级特征聚合网络，充分利用不同尺度的特征信息，完成特征的有效聚合，最终联合多通道特征信息输出检测结果。构建包含10类目标的低空伪装目标偏振图像数据集PICO(Polarization Image of Camouflaged Objects)。在PICO数据集上的实验结果表明，新方法可以有效检测伪装目标，mAP_(0.5:0.95)达到52.0%,mAP_(0.5)达到91.5%,检测速率达到55.0帧/s,满足实时性要求。
Keyword ：
伪装目标检测 伪装目标检测 偏振成像 偏振成像 无人机 无人机 深度学习 深度学习 特征增强 特征增强 特征聚合 特征聚合
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| GB/T 7714 | 沈英 , 刘贤财 , 王舒 et al. 基于偏振编码图像的低空伪装目标实时检测 [J]. | 兵工学报 , 2024 , 45 (05) : 1374-1383 . |
| MLA | 沈英 et al. "基于偏振编码图像的低空伪装目标实时检测" . | 兵工学报 45 . 05 (2024) : 1374-1383 . |
| APA | 沈英 , 刘贤财 , 王舒 , 黄峰 . 基于偏振编码图像的低空伪装目标实时检测 . | 兵工学报 , 2024 , 45 (05) , 1374-1383 . |
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Polarization can improve the autonomous reconnaissance capability of unmanned aerial vehicle, but it is easily interfered by the variation of detection angle and target materials, which affects the robustness of polarization detection. In this paper, a real-time low-altitude camouflaged target detection algorithm of YOLO-Polarization based on polarized images is proposed. The coded image fused with multi-polarization direction information is used as input, the 3D convolution module is applied to extract the connection features from the different polarization direction images, and a feature enhancement module (FEM) is introduced to further enhance the multi-level features. In addition, the cross-level feature aggregation network is adopted to make full use of the feature information of different scales to complete the effective aggregation of features, and finally combined with multi-channel feature information output detection results. A dataset consisting of polarized images of low-altitude camouflaged targets (PICO) which include 10 types of targets is constructed. The experimental results based on PICO dataset show that the proposed method can effectively detect the camouflaged targets, with mAP0. 5:0. 95 up to 52. 0% and mAP0. 5 up to 91. 5% . The detection rate achieves 55. 0 frames / s, which meets the requirement of real-time detection. © 2024 China Ordnance Industry Corporation. All rights reserved.
Keyword ：
Aircraft detection Aircraft detection Antennas Antennas Deep learning Deep learning Feature extraction Feature extraction Image enhancement Image enhancement Polarization Polarization Signal detection Signal detection Unmanned aerial vehicles (UAV) Unmanned aerial vehicles (UAV)
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| GB/T 7714 | Shen, Ying , Liu, Xiancai , Wang, Shu et al. Real-time Detection of Low-altitude Camouflaged Targets Based on Polarization Encoded Images [J]. | Acta Armamentarii , 2024 , 45 (5) : 1374-1383 . |
| MLA | Shen, Ying et al. "Real-time Detection of Low-altitude Camouflaged Targets Based on Polarization Encoded Images" . | Acta Armamentarii 45 . 5 (2024) : 1374-1383 . |
| APA | Shen, Ying , Liu, Xiancai , Wang, Shu , Huang, Feng . Real-time Detection of Low-altitude Camouflaged Targets Based on Polarization Encoded Images . | Acta Armamentarii , 2024 , 45 (5) , 1374-1383 . |
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Lutein is a natural antioxidant that has numerous benefits for human health. Heterotrophic Chlorella sorokiniana has the advantage of high purity and production of lutein. In contrast, the production of lutein in Chlorella sorokiniana mainly depends on two factors: biomass productivity and lutein content. However, conventional approaches such as the optical density method for measuring biomass productivity and high-performance liquid chromatography for measuring lutein content suffer from drawbacks, including complex procedures and limited timeliness. A visible near-infrared dual-mode snapshot multispectral imaging detection system was constructed to rapidly and non-destructively determine the variations in lutein production during the growth process of Chlorella sorokiniana. Based on the spectral response range, the visible camera was used to obtain the spectral information image of lutein content, and the near-infrared camera was used to obtain the spectral information image of biomass productivity to build a visible near-infrared dual mode multispectral dataset containing biomass productivity and lutein content information. To address the issue of wide spectral range and limited wavelengths in the snapshot multispectral camera used in the system, a novel approach combining sequential floating forward selection with a modified successive projections algorithm (mSPA) was proposed. A comparative study was conducted, evaluating mSPA against successive projections algorithm, genetic algorithm, and random frog algorithm for wavelength selection. Multiple linear regression and extreme learning machine models were constructed based on the selected feature wavelengths. Finally, the optimal predictive models for biomass productivity and lutein content were used to generate a visualization distribution map of lutein production in Chlorella sorokiniana. The results indicated that when using near-infrared and visible cameras for biomass productivity and lutein detection in Chlorella sorokiniana, the mSPA algorithm consistently yielded fewer feature wavelengths for both biomass productivity and lutein and achieved the highest prediction accuracy. The optimal models of biomass productivity and lutein content were established using the mSPA-selected feature wavelengths in combination with an extreme learning machine. The corresponding coefficients of determination for the prediction sets were 0. 947 for biomass productivity and 0. 907 for lutein, with root mean square errors of 0. 698 g . L-1 and 0. 077 mg . g(-1) and residual prediction deviations of 3. 535 and 3. 338, respectively. The models demonstrated good predictive capabilities. The visualization distribution successfully achieved intuitive monitoring of lutein production variations in Chlorella sorokiniana which is beneficial for online detection of lutein content in practical production scenarios. The mSPA algorithm, employed in the snapshot multispectral detection of biomass productivity and lutein content in Chlorella sorokiniana, effectively avoided the incorrect selection and omission of feature wavelengths by evaluating each sorted wavelength individually, thereby improving the prediction accuracy of the models. This approach provides a new wavelength selection strategy for applying snapshot multispectral imaging technology.
Keyword ：
Chlorella sorokiniana Chlorella sorokiniana Feature wavelengths Feature wavelengths Lutein production Lutein production Snapshot multispectral Snapshot multispectral
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| GB/T 7714 | Shen Ying , Zhan Xiu-xing , Huang Chun-hong et al. Rapid Determination of Chlorella Sorokiniana Lutein Production Based on Snapshot Multispectral Feature Wavelengths [J]. | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2024 , 44 (8) : 2216-2223 . |
| MLA | Shen Ying et al. "Rapid Determination of Chlorella Sorokiniana Lutein Production Based on Snapshot Multispectral Feature Wavelengths" . | SPECTROSCOPY AND SPECTRAL ANALYSIS 44 . 8 (2024) : 2216-2223 . |
| APA | Shen Ying , Zhan Xiu-xing , Huang Chun-hong , Xie You-ping , Guo Cui-xia , Huang Feng . Rapid Determination of Chlorella Sorokiniana Lutein Production Based on Snapshot Multispectral Feature Wavelengths . | SPECTROSCOPY AND SPECTRAL ANALYSIS , 2024 , 44 (8) , 2216-2223 . |
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Spectral reconstruction technology extracts rich detail information from limited spectral bands, thereby enhancing both of the image quality and the resolution capabilities. It finds application in non-destructive testing, elevating the precision and robustness of detection. Current studies primarily focus on improving the local information perception of convolutional neural networks or modeling long-distance dependencies with Transformer. However, such approaches fail to effectively integrate global-local modeling information, resulting in poor accuracy in image reconstruction. This paper introduces a Progressive CNN-Transformer Alternating Reconstruction Network (PCTARN) to alternately utilize robust convolutional attention and transpose Transformer self-attention. A Dual-Path CNN-Transformer Alternating Reconstruction Module (DPCTARM) is proposed to dynamically introduce global-local dynamic priors at various levels to facilitate extracting high- and low-frequency features. This enhancement effectively strengthens PCTARN's capability to discern valuable signals. To verify the proposed method, a spectral dataset based on seven selected red tide algae is collected. And a peak signal-to-noise ratio (PSNR) metric of 34.58 dB is achieved, which is at least 0.44 dB higher than the methods such as MAUN and MST++. While the Params and FLOPS are reduced by over 41.9 % and 38.4 %, respectively. Since the performance of the proposed PCTARN depends not only on image quality but also on spectral fidelity, an application of spectral detection on red tide are conducted for this purpose. Four feature bands are selected from multispectral images and reconstructed into 20-band hyperspectral images by using PCTARN. Species identification and cell concentration detection are conducted based on the reconstructed images. The results demonstrate that PCTARN can enhance the spatial signal and spectral peak differences of red tide samples, achieving an identification accuracy of 94.21 % and a coefficient of determination (R2) of 0.9660 in species identification and cell concentration detection, which are respectively improved by 11.55 % and 11.59 % compared to those of 4-band multispectral detection.
Keyword ：
CNN-Transformer interaction CNN-Transformer interaction Multiscale fusion Multiscale fusion Red tide detection Red tide detection Spectral imaging Spectral imaging Spectral reconstruction Spectral reconstruction
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| GB/T 7714 | Shen, Ying , Zhong, Ping , Zhan, Xiuxing et al. Progressive CNN-transformer alternating reconstruction network for hyperspectral image reconstruction-A case study in red tide detection [J]. | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2024 , 134 . |
| MLA | Shen, Ying et al. "Progressive CNN-transformer alternating reconstruction network for hyperspectral image reconstruction-A case study in red tide detection" . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 134 (2024) . |
| APA | Shen, Ying , Zhong, Ping , Zhan, Xiuxing , Chen, Xu , Huang, Feng . Progressive CNN-transformer alternating reconstruction network for hyperspectral image reconstruction-A case study in red tide detection . | INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION , 2024 , 134 . |
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