Journals >Laser & Optoelectronics Progress
ing at the difficulty of extracting the key contour features of printed circuit boards, an algorithm for transforming the folded edge into the boundary and extracting the key contour feature points is proposed. First, the algorithm establishes a topological structure of the original point cloud data of the printed circuit board by using k dimensional-tree, and realize fast search of the closest k neighborhood points. Pass-through filtering algorithm is used to complete the pre-processing of the printed circuit board point cloud. Second, Random Sample Consensus algorithm is used to extract the plane features with the largest area in the printed circuit board separately, so that the key contour features are spatially separated. The point clustering of the fold edge feature is completed by Euclidean clustering based on normal angle and realize the idea of transforming the folded edge into the boundary. Finally, according to the relationship between set threshold and vector angle between k neighborhood points, one can determine whether the query point belongs to the boundary contour feature point. Experimental results show that the proposed algorithm can extract the key contour feature line of printed circuit board point cloud more completely.
.ing at the shortcomings of the existing dehazing algorithms, such as transmittance over-estimation, sky color distortion, and poor real-time, a fast and efficient real-time video dehazing algorithm based on pyramid model is proposed. First, pyramid down-sampling is used to obtain the reduced image. Pseudo dehazing image and dark channel confidence are introduced as correction factors to obtain the coarse transmission of the reduced image. Second, the reduced image is restored to original size and refined by a joint bilateral filtering. Finally, atmospheric scattering model and the inter-frame video dehazing theory are combined to restore the degraded video. Experiment results show that, this method can completely dehaze on a variety of scenes. Compared with other algorithms, the improvement of peak signal-to-noise ratio and average structure similarity of this algorithm are 20.153% and 14.056%, respectively. The proposed method is fast, stable, and suitable for real-time video dehazing.
.ing at the problem of large computation and complicated process of segmentation for whole tumor lesion in segmented magnetic resonance imaging (MRI) three-dimensional images, a fully automatic segmentation algorithm based on deep learning is proposed. A dual pathway three-dimensional convolutional neural network model is constructed on the dilated convolution path filled with jagged holes to extract multi-scale image blocks for training and capture large-scale spatial information. The shallow features are superimposed to the end of the network by using the identity mapping feature of dense connection. The swollen area, enhanced area, core area, and cystic area are segmented in the multi-modal MRI image. The model is segmented and tested in the BraTS 2018 dataset. The results show that the average Dice coefficients of the whole tumor area, core area and enhanced tumor area segmented by the model are about 0.90, 0.73 and 0.71, respectively, which is equal to the performance of the current algorithms and has a high degree of automation integration.
.ing at the shortcomings of existing methods for brain tumor image segmentation, this paper proposes a brain tumor image segmentation algorithm based on an improved convolutional neural network. First, DenseNet and U-net network structures are combined to improve the extraction capability for image features. Second, in order to expand the receptive field of the convolution kernel, the cavity convolution is adopted. Moreover, the segmentation results are further finely segmented and output by a fully connected conditional random field recurrent neural networks, thereby obtaining an accurate brain tumor segmentation region. Experimental results show that compared with traditional deep learning methods, the proposed algorithm has an average Dice up to 91.64%, and has a better improvement in accuracy.
.ing at the problems of low image contrast, color imbalance, and noise in low-light conditions, a low-light image enhancement model based on multi-branch all convolutional neural network (MBACNN) is proposed. The model is an end-to-end model, including feature extraction module (FEM), enhancement module (EM), fusion module (FM), and noise extraction module (NEM). By training the synthesized low-light and high-definition image sample, the model parameters are continuously adjusted according to the loss value of the verification set to obtain the optimal model, and then the synthetic low-light image and the real low-light image are tested. Experimental results show that compared with traditional image enhancement algorithms, the proposed model can effectively improve image contrast, adjust color imbalance, and remove noise. Both subjective visual and objective image quality evaluation indicators are further improved.
.ing at the problem of poor visual effect and low image quality of existing low-light images, a low-light image enhancement algorithm based on cascaded residual generative adversarial network is proposed. The algorithm uses constructed cascaded residual convolutional neural network as generator network and improved PatchGAN as discriminator network. First, training samples are synthesized through normal-light image on the basis of Retinex theory. Then, low-light images are converted from red-green-blue space to hue-saturation-value color space. Meanwhile, keeping hue and saturation unchanged, the value component is enhanced through the cascaded residual generator network. Besides, low-light image is enhanced through the way of discriminator network supervising generator network. They struggle against each other to promote the capability of generator network to enhance the low-light image. Experimental results show that the proposed enhancement algorithm obtains better visual effects and contrast in terms of synthetic low-light images and natural low-light images. Especially, for the synthetic low-light images, the proposed algorithm is obviously superior to other comparison algorithms in terms of peak signal-to-noise ratio and structural similarity.
.ing at the problems that traditional spectral polarization imaging technology requires dynamic modulation, low luminous flux and limited spectral resolution, a new imaging method based on computational spectral imaging technology and pixel-level polarization detection is proposed. The dual-channel format is used for obtaining high-resolution spatial, spectral, and polarization target information through single imaging. Further, a dual-channel experimental device with a coded-aperture spectral polarization imaging channel and a polarization imaging channel is established to obtain spectral data cubes with four polarization states in 25 bands in the range of 450-650 nm, as well as the polarization degree and polarization angle of each band. The spectral resolution of proposed method is better than 10 nm, and the spectral reconstruction accuracy is approximately 86.3%. Furthermore, the spectral reconstruction accuracy is observed to improve by 10.5 percentage points when compared with that of the single-channel imaging method.
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