Image restoration is the problem of restoring a real degraded image.Previous studies mostly focused on single distortion.However,most of the real images experience multiple distortions,and single distortion image rest...Image restoration is the problem of restoring a real degraded image.Previous studies mostly focused on single distortion.However,most of the real images experience multiple distortions,and single distortion image restoration algorithms can not effectively improve the image quality.Moreover,few existing hybrid distortion image restoration algorithms can not deal with single distortion.Therefore,an end-to-end pipeline network based on stagewise training is proposed in this paper.Specifically,the network selects three typical image restoration tasks:denoising,inpainting,and super resolution.The whole training process is divided into single distortion training,hybrid distortion training of two types,and hybrid distortion training of three types.The design of loss function draws on the idea of deep supervision.Experimental results prove that the proposed method is not only superior to other methods in hybrid-distorted image restoration,but also suitable for single distortion image restoration.展开更多
Sparse coding is a prevalent method for image inpainting and feature extraction,which can repair corrupted images or improve data processing efficiency,and has numerous applications in computer vision and signal proce...Sparse coding is a prevalent method for image inpainting and feature extraction,which can repair corrupted images or improve data processing efficiency,and has numerous applications in computer vision and signal processing.Recently,sev-eral memristor-based in-memory computing systems have been proposed to enhance the efficiency of sparse coding remark-ably.However,the variations and low precision of the devices will deteriorate the dictionary,causing inevitable degradation in the accuracy and reliability of the application.In this work,a digital-analog hybrid memristive sparse coding system is pro-posed utilizing a multilevel Pt/Al_(2)O_(3)/AlO_(x)/W memristor,which employs the forward stagewise regression algorithm:The approxi-mate cosine distance calculation is conducted in the analog part to speed up the computation,followed by high-precision coeffi-cient updates performed in the digital portion.We determine that four states of the aforementioned memristor are sufficient for the processing of natural images.Furthermore,through dynamic adjustment of the mapping ratio,the precision require-ment for the digit-to-analog converters can be reduced to 4 bits.Compared to the previous system,our system achieves higher image reconstruction quality of the 38 dB peak-signal-to-noise ratio.Moreover,in the context of image inpainting,images containing 50%missing pixels can be restored with a reconstruction error of 0.0424 root-mean-squared error.展开更多
提出一种新的多类分类AdaBoost算法——使用多类分类指数损失函数的前向逐步叠加模型FSAMME(forward stagewise additive modeling using a multi-class exponential loss function)。该算法是基于原始的两类分类AdaBoost算法归结为使...提出一种新的多类分类AdaBoost算法——使用多类分类指数损失函数的前向逐步叠加模型FSAMME(forward stagewise additive modeling using a multi-class exponential loss function)。该算法是基于原始的两类分类AdaBoost算法归结为使用两类分类指数损失函数的前向逐步叠加模型的统计学观点,将两类分类的前向逐步叠加模型自然扩展到多类分类情况下得到的,并采用多类指数损失函数和前向逐步叠加模型对FSAMME进行了详细的理论证明。该算法大大降低对弱分类器的精度要求,只需每个弱分类器的精度比随机猜测好;算法简单明了,不用把多类问题转化为多个两类问题,而是直接求解多类分类问题,大大减小计算复杂度和计算量。通过对基准数据库的测试分类及航空发动机故障样本的诊断,结果表明:FSAMME算法一方面可达到较高的分类诊断准确率,其准确率明显高于AdaBoost.M1,略高于AdaBoost.MH;另一方面可大大减小计算成本,满足在线快速分类诊断的要求。展开更多
文摘Image restoration is the problem of restoring a real degraded image.Previous studies mostly focused on single distortion.However,most of the real images experience multiple distortions,and single distortion image restoration algorithms can not effectively improve the image quality.Moreover,few existing hybrid distortion image restoration algorithms can not deal with single distortion.Therefore,an end-to-end pipeline network based on stagewise training is proposed in this paper.Specifically,the network selects three typical image restoration tasks:denoising,inpainting,and super resolution.The whole training process is divided into single distortion training,hybrid distortion training of two types,and hybrid distortion training of three types.The design of loss function draws on the idea of deep supervision.Experimental results prove that the proposed method is not only superior to other methods in hybrid-distorted image restoration,but also suitable for single distortion image restoration.
基金This work was supported by the National Key R&D Program of China(Grant No.2019YFB2205100)in part by Hubei Key Laboratory of Advanced Memories.
文摘Sparse coding is a prevalent method for image inpainting and feature extraction,which can repair corrupted images or improve data processing efficiency,and has numerous applications in computer vision and signal processing.Recently,sev-eral memristor-based in-memory computing systems have been proposed to enhance the efficiency of sparse coding remark-ably.However,the variations and low precision of the devices will deteriorate the dictionary,causing inevitable degradation in the accuracy and reliability of the application.In this work,a digital-analog hybrid memristive sparse coding system is pro-posed utilizing a multilevel Pt/Al_(2)O_(3)/AlO_(x)/W memristor,which employs the forward stagewise regression algorithm:The approxi-mate cosine distance calculation is conducted in the analog part to speed up the computation,followed by high-precision coeffi-cient updates performed in the digital portion.We determine that four states of the aforementioned memristor are sufficient for the processing of natural images.Furthermore,through dynamic adjustment of the mapping ratio,the precision require-ment for the digit-to-analog converters can be reduced to 4 bits.Compared to the previous system,our system achieves higher image reconstruction quality of the 38 dB peak-signal-to-noise ratio.Moreover,in the context of image inpainting,images containing 50%missing pixels can be restored with a reconstruction error of 0.0424 root-mean-squared error.
文摘提出一种新的多类分类AdaBoost算法——使用多类分类指数损失函数的前向逐步叠加模型FSAMME(forward stagewise additive modeling using a multi-class exponential loss function)。该算法是基于原始的两类分类AdaBoost算法归结为使用两类分类指数损失函数的前向逐步叠加模型的统计学观点,将两类分类的前向逐步叠加模型自然扩展到多类分类情况下得到的,并采用多类指数损失函数和前向逐步叠加模型对FSAMME进行了详细的理论证明。该算法大大降低对弱分类器的精度要求,只需每个弱分类器的精度比随机猜测好;算法简单明了,不用把多类问题转化为多个两类问题,而是直接求解多类分类问题,大大减小计算复杂度和计算量。通过对基准数据库的测试分类及航空发动机故障样本的诊断,结果表明:FSAMME算法一方面可达到较高的分类诊断准确率,其准确率明显高于AdaBoost.M1,略高于AdaBoost.MH;另一方面可大大减小计算成本,满足在线快速分类诊断的要求。
