•We proposed new singular value decomposition in the reduced biquaternion domain.•We proposed new sparse representation model using the reduced biquaternion algebra.•We proposed reduced biquaternion ...orthogonal matching pursuit algorithm.•The proposed model is extended to the application of color image denoising.
Traditional sparse representation models treat color image either represent color channels independently using the monochromatic model or concatenate color channels using the concatenation model. However, these two strategies cannot make full use of the correlation among the three color channels. In this paper, we propose a novel sparse representation model for color image based on reduced biquaternion. The advances of the proposed model are in two aspects: 1) compared with quaternion algebra, reduced biquaternion algebra is commutative algebra which has a low computation cost; 2) benefited from reduced biquaternion representation, the proposed model can treat the color image as a whole. We firstly extend the singular value decomposition (SVD) to the reduced biquaternion domain (RBSVD). The framework of the proposed model contains two major stages: first, reduced biquaternion orthogonal matching pursuit (RBOMP) is proposed in the stage of sparse coding; second, generalized K-means clustering for RBSVD (K-RBSVD) is proposed in the stage of dictionary training. Afterward, a new color image denoising algorithm is developed to demonstrate the effectiveness of the proposed model. The experimental results demonstrate that the proposed sparse representation model achieves state-of-the-art denoising performance in terms of both quantitative metrics and visual quality.
In this paper, we propose a novel paper currency defect detection algorithm using quaternion uniform strength. We first build paper currency image preprocessing integration framework which includes ...intensity balancing, paper currency location, and geometric correction. We then propose a global–local paper currency image registration algorithm by moving key areas within certain range which can eliminate the false difference effectively. Finally, the quaternion uniform strength is calculated by using quaternion convolution edge detector. The defect degree of paper currency is determined by using the quaternion uniform color difference. The proposed algorithm is tested using different datasets from five countries: CNY, USD, EUR, VND, and RUB. The experimental results demonstrate that the proposed algorithm yields better results than the existing state-of-the-art paper currency defect detection techniques. The demo of the proposed paper currency defect detection algorithm will be publicly available.
The end-to-end convolutional neural network models have been widely used in single image de-raining, which can extract clean background images from rainy images. However, they suffer from gradient ...vanishing with increased network depth. With the aim of tackling this problem, this paper proposes an effective rain removal algorithm based on dual parallel branch residual overlay network (DBRONet). Firstly, the two parallel branches with different functions are combined with increasing the width of the network, which can reduce the depth of the network effectively. Secondly, the upper branch uses multi-scale rain streak extraction blocks (MRSEB) composed of multi-scale residual blocks and extracts rain streaks of different densities, sizes and directions in the rainy images. The lower branch utilizes dilated convolution attention residual block (DARB) to expand the receptive field and obtains more context information without increasing the depth of the network. Finally, the de-raining image is obtained by superposition features of the two branches and the original image. Experimental results on synthetic and real datasets show that DBRONet can effectively reduce the depth of the network and the number of parameters. Compared with the existing methods in terms of quantitative and qualitative indicators, it has achieved the most advanced results. When comparing with other methods on Rain100H, Rain100L, Rain12, Rain1400 with the improvements of 0.76 dB, 0.29 dB, 0.11 dB and 0.25 dB on PSNR value and 0.4%, 0.3%, 0.4%, 1.2% on SSIM value, respectively. The source code can be found at
https://github.com/RemeberMeX
/DBRONet.
The new image de-nosing algorithm based on improved deep convolutional neural network in the monogenic wavelet domain is proposed in this paper. The monogenic wavelet transform was employed to ...describe the amplitude and phase information of the noisy image. Then, the amplitude and phase information are simultaneously used as input of proposed improved convolutional neural network for denoising. Finally, the monogenic wavelet inverse transform is used to obtain the denoised image. The experimental results illustrate that the proposed algorithm achieves superior performance both in visual quality and objective peak signal-to-noise ratio values, compared with other state-of-the-art de-noising algorithms.
Most of the existing deraining methods cannot preserve the details of the image while removing the rain streaks. To solve this problem, we propose a single image de-raining method with dual U-Net ...generative adversarial network (DU-GAN). By using two U-Net with stronger learning ability as our generator DU-GAN can not only accurately remove more rain streaks but also preserve image details. The network can make full use of image information and extract complete image features. The adversarial loss function using the proposed dual U-Net generator is utilized to generate de-rained images which are close to the ground truth. Furthermore, to obtain the better visual effects of the generated image, The L1 and structure similarity loss functions which are consistent with the human visual effect are applied to generate the final output. The synthetic rainy image datasets and real rainy image datasets are used to evaluate the effectiveness of the proposed network in the experiments. The quantitative and visual experimental results show that the proposed single image deraining method achieves state-of-the-art compared with the other single image deraining methods. The source code can be found at
https://github.com/LuBei-design/DU-GAN
.
The weld penetration rate is an important evaluation criterion for welding quality. However, it is difficult to identify the weld penetration state during GTAW welding process. This paper presents a ...new penetration recognition method based on time and spectrum images of arc sound using deep learning for DC GTAW welding. The time domain and spectrum images of the three penetration states from the non-periodic arc sound were used as the dataset for the penetration prediction model. VGG16, AlexNet, and custom convolutional neural network (CNN) were used to extract image features, and softmax was used to classify images for penetration recognition. The influence of image feature extraction networks, input methods, and different sampling methods on the recognition accuracy was deeply analyzed. The results show that the overall validation accuracy of the proposed model is approximately 96.2%. Particularly, the validation accuracy of the model in the excessive penetration state is approximately 100%. This study provides a new and feasible method for the online detection of weld penetration during the GTAW welding process.
In order to improve the performance of the banknote classification, this paper presents a new feature extraction method based on quaternion wavelet transform (QWT). The QWT yields one shift invariant ...magnitude and three phases based on quaternion algebra. The generalized Gaussian density (GGD) is applied to capture the statistical characteristics of QWT coefficients. The neural network is used as classifier in the framework of banknote classification. Experimental results demonstrate its effectiveness and the proposed method obtains a higher recognition rate in the banknote classification.
•New feature extraction method based on QWT is proposed.•The GGD is used to describe the feature of the transformed coefficients.•New banknote classification system is proposed based on QWT.
In this paper, we propose a new framework for banknote classification based on quaternion wavelet transform (QWT) and deep convolutional neural network. Firstly, the QWT is applied to describe the ...phase and magnitude of different banknote images which has inherent directional sensitivity and multi-scale framework. Then we design a deep convolutional neural network which is trained on banknote images along with the magnitude and phase of quaternion wavelet coefficients. We assign the neural weights on the output probabilities of deep convolutional neural network and update these weights by utilizing the back propagation algorithm. Finally, the trained networks with decision of a weighted sum and the magnitude and the phase of quaternion wavelet networks are utilized for banknote image classification. The performance of our algorithm is experimentally verified on a variety of banknote databases. Experimental results show that the proposed algorithm achieves superior performance compared with other state-of-the-art banknote classification algorithms. The proposed algorithm can also satisfy the real-time requirements of the banknote sorting system.
Memristor is a fundamental circuit element in addition to resistor, capacitor, and inductor. As it can remember its resistance state even encountering a power off, memristor has recently received ...widespread applications from non-volatile memory to neural networks. The current memristor family mainly comprises resistive memristor, polymeric memristor, ferroelectric memristor, manganite memristor, resonant-tunneling diode memristor, and spintronic memristor in terms of the materials the device is made of. In order to help researcher better understand the physical principles of the memristor, and thus to provide a promising prospect for memristor devices, this paper presents an overview of memristor materials properties, switching mechanisms, and potential applications. The performance comparison among different memristor members is also given.
Dynamic scene deblurring is a significant technique in the field of computer vision. The multi-scale strategy has been successfully extended to the deep end-to-end learning-based deblurring task. Its ...expensive computation gives birth to the multi-patch framework. The success of the multi-patch framework benefits from the local residual information passed across the hierarchy. One problem is that the finest levels rarely contribute to their residuals so that the contributions of the finest levels to their residuals are excluded by coarser levels, which limits the deblurring performance. To this end, we substitute the nested module blocks, whose powerful and complex representation ability is utilized to improve the deblurring performance, for the building blocks of the encoder-decoders in the multi-patch network. Additionally, the attention mechanism is introduced to enable the network to differentiate blur across the whole blurry image from dynamic scene, thereby further improving the ability to handle the motion object blur. Our modification boosts the contributions of the finest levels to their residuals and enables the network to learn different weights for feature information extracted from spatially-varying blur image. Extensive experiments show that the improved network achieves competitive performance on the GoPro dataset according to PSNR and SSIM.
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