Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color...
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Due to the selective absorption of light and the existence of a large number of floating media in sea water, underwater images often suffer from color casts and detail blurs. It is therefore necessary to perform color correction and detail restoration. However,the existing enhancement algorithms cannot achieve the desired results. In order to solve the above problems, this paper proposes a multi-stream feature fusion network. First, an underwater image is preprocessed to obtain potential information from the illumination stream, color stream and structure stream by histogram equalization with contrast limitation, gamma correction and white balance, respectively. Next, these three streams and the original raw stream are sent to the residual blocks to extract the features. The features will be subsequently fused. It can enhance feature representation in underwater images. In the meantime, a composite loss function including three terms is used to ensure the quality of the enhanced image from the three aspects of color balance, structure preservation and image smoothness. Therefore, the enhanced image is more in line with human visual ***, the effectiveness of the proposed method is verified by comparison experiments with many stateof-the-art underwater image enhancement algorithms. Experimental results show that the proposed method provides superior results over them in terms of MSE,PSNR, SSIM, UIQM and UCIQE, and the enhanced images are more similar to their ground truth images.
Aiming to solve the poor performance of low illumination enhancement algorithms on uneven illumination images,a low-light image enhancement(LIME)algorithm based on a residual network was *** algorithm constructs a dee...
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Aiming to solve the poor performance of low illumination enhancement algorithms on uneven illumination images,a low-light image enhancement(LIME)algorithm based on a residual network was *** algorithm constructs a deep network that uses residual modules to extract image feature information and semantic modules to extract image semantic information from different ***,a composite loss function was also designed for the process of low illumination image enhancement,which dynamically evaluated the loss of an enhanced image from three factors of color,structure,and *** ensures that the model can correctly enhance the image features according to the image semantics,so that the enhancement results are more in line with the human visual *** results show that compared with the state-of-the-art algorithms,the semantic-driven residual low-light network(SRLLN)can effectively improve the quality of low illumination images,and achieve better subjective and objective evaluation indexes on different types of images.
Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on l...
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Various deep learning models have been proposed for the accurate assisted diagnosis of early-stage Alzheimer’s disease(AD).Most studies predominantly employ Convolutional Neural Networks(CNNs),which focus solely on local features,thus encountering difficulties in handling global *** contrast to natural images,Structural Magnetic Resonance Imaging(sMRI)images exhibit a higher number of channel ***,during the Position Embedding stage ofMulti Head Self Attention(MHSA),the coded information related to the channel dimension is *** tackle these issues,we propose theRepBoTNet-CESA network,an advanced AD-aided diagnostic model that is capable of learning local and global features *** combines the advantages of CNN networks in capturing local information and Transformer networks in integrating global information,reducing computational costs while achieving excellent classification ***,it uses the Cubic Embedding Self Attention(CESA)proposed in this paper to incorporate the channel code information,enhancing the classification performance within the Transformer ***,the RepBoTNet-CESA performs well in various AD-aided diagnosis tasks,with an accuracy of 96.58%,precision of 97.26%,and recall of 96.23%in the AD/NC task;an accuracy of 92.75%,precision of 92.84%,and recall of 93.18%in the EMCI/NC task;and an accuracy of 80.97%,precision of 83.86%,and recall of 80.91%in the AD/EMCI/LMCI/NC *** demonstrates that RepBoTNet-CESA delivers outstanding outcomes in various AD-aided diagnostic ***,our study has shown that MHSA exhibits superior performance compared to conventional attention mechanisms in enhancing ResNet ***,the Deeper RepBoTNet-CESA network fails to make further progress in AD-aided diagnostic tasks.
Orientation-dependent transport properties induced by anisotropic molecules are enticing in single-molecule ***,using the first-principles method,we theoretically investigate spin transport properties and photorespons...
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Orientation-dependent transport properties induced by anisotropic molecules are enticing in single-molecule ***,using the first-principles method,we theoretically investigate spin transport properties and photoresponse characteristics in trimesic acid magnetic single-molecule junctions with different molecular adsorption orientations and electrode contact *** transport calculations indicate that a single-molecule switch and a significant enhancement of spin transport and photoresponse can be achieved when the molecular adsorption orientation changes from planar geometry to upright *** maximum spin polarization of current and photocurrent in upright molecular junctions exceeds 90%.Moreover,as the Ni tip electrode moves,the tunneling magnetoresistance of upright molecular junctions can be increased to 70%.The analysis of the spin-dependent PDOS elucidates that the spinterfaces between organic molecule and ferromagnetic electrodes are modulated by molecular adsorption orientation,where the molecule in upright molecular junctions yields higher spin *** theoretical work paves the way for designing spintronic devices and optoelectronic devices with anisotropic functionality base on anisotropic molecules.
Short-term residential load forecasting is essential to demand side response. However, the frequent spikes in the load and the volatile daily load patterns make it difficult to accurately forecast the load. To deal wi...
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In this paper, a novel four-level distributed game decision-making framework of dynamic peer-to-peer (P2P) carbon emission right (CER) sharing is proposed for microgrid clusters (MGCs), which consists of four phases: ...
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Artificial intelligence (AI) has been a key research area since the 1950s, initially focused on using logic and reasoning to create systems that understand language, control robots, and offer expert advice. With the r...
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2D human posture detection, a challenging task in computer vision, aims to identify human body key points and is widely used. The HRNet model performs well in human key point detection. To enhance its effect in human ...
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Unsupervised learning methods in computer vision have achieved remarkable success, exceeding the performance of supervised learning methods. It is noteworthy that current unsupervised learning methods share certain si...
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Room layout estimation seeks to infer the overall spatial configuration of indoor scenes using perspective or panoramic images. As the layout is determined by the dominant indoor planes, this problem inherently requir...
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