Removing clouds from remote sensing images poses a significant challenge in image analysis. Despite the widespread application of deep learning methods in the field of cloud removal, their ability to extract and integ...
Edge computing fulfills the urgent need of users for low-latency and high-quality computing services by transferring tasks from end devices to the edge side of the network for processing through task offloading techni...
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Content Aiming at the problem that iris images are easily affected by eyelid and eyelash noise, which leads to low positioning accuracy and poor stability, an iris positioning network based on YOLOv4 model is proposed...
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The traditional Monte Carlo rendering method accurately renders 3D scenes but suffers from slow rendering speeds, limiting its suitability for high frame rate applications. Light probes offer a solution for achieving ...
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The triangle mesh model with huge volume is not conducive to computer storage, analysis and rendering, and the mesh simplification method can effectively reduce the complexity of the triangle mesh model, but there are...
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In addressing the limited receptive field of single-layer convolution in EEG emotion recognition, the need to stack multiple layers of convolution for expanding the receptive field poses challenges of increased parame...
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In addressing the issue of unstable boundary adherence in traditional superpixel segmentation algorithms, this paper proposes a novel region-adaptive superpixel segmentation algorithm based on density clustering for c...
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Heads-up computing aims to provide synergistic digital assistance that minimally interferes with users' on-the-go daily activities. Currently, the input modalities of heads-up computing are mainly voice and finger...
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Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acq...
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Images obtained from hyperspectral sensors provide information about the target area that extends beyond the visible portions of the electromagnetic ***,due to sensor limitations and imperfections during the image acquisition and transmission phases,noise is introduced into the acquired image,which can have a negative impact on downstream analyses such as classification,target tracking,and spectral *** in hyperspectral images(HSI)is modelled as a combination from several sources,including Gaussian/impulse noise,stripes,and *** HSI restoration method for such a mixed noise model is ***,a joint optimisation framework is proposed for recovering hyperspectral data corrupted by mixed Gaussian-impulse noise by estimating both the clean data as well as the sparse/impulse noise ***,a hyper-Laplacian prior is used along both the spatial and spectral dimensions to express sparsity in clean image ***,to model the sparse nature of impulse noise,anℓ_(1)−norm over the impulse noise gradient is *** the proposed methodology employs two distinct priors,the authors refer to it as the hyperspectral dual prior(HySpDualP)*** the best of authors'knowledge,this joint optimisation framework is the first attempt in this *** handle the non-smooth and nonconvex nature of the generalℓ_(p)−norm-based regularisation term,a generalised shrinkage/thresholding(GST)solver is ***,an efficient split-Bregman approach is used to solve the resulting optimisation *** results on synthetic data and real HSI datacube obtained from hyperspectral sensors demonstrate that the authors’proposed model outperforms state-of-the-art methods,both visually and in terms of various image quality assessment metrics.
Accurate segmentation of brain tumors is crucial for their early diagnosis. Multi-modal MRI images can provide complementary information, which is essential for improving segmentation accuracy. This paper presents a b...
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