In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and ...In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising. First, the wavelet transform is adopted to decompose the image of refuge chamber, of which low frequency component remains unchanged. Then, three high-frequency components are treated by bilateral filtering, and the image is reconstructed. The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image, while providing better visual effect. This is superior to using either bilateral filtering or wavelet transform alone. It is useful for perfecting emergency refuge system of coal mines.展开更多
Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation ...Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.展开更多
Deviation is essential to classic soft threshold denoising in wavelet domain. Texture features ofnoised image denoised by wavelet transform were weakened. Gibbs effect is distinct at edges of image.Image blurs compari...Deviation is essential to classic soft threshold denoising in wavelet domain. Texture features ofnoised image denoised by wavelet transform were weakened. Gibbs effect is distinct at edges of image.Image blurs comparing with original noised image. To solve the questions, a blind denoising method basedon single-wavelet transform and multiwavelets transform was proposed. The method doesn’t depend onsize of image and deviation to determine threshold of wavelet coefficients, which is different from classicalsoft-threshold denoising in wavelet domain. Moreover, the method is good for many types of noise. Gibbseffect disappeared with this method, edges of image are preserved well, and noise is smoothed andrestrained effectively.展开更多
Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After makin...Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After making stationary wavelet transform to an infrared image,denoising is done by the proposed method of double-threshold shrinkage in detail coefficient matrixes that have high noisy intensity.For the approximation coefficient matrix with low noisy intensity,enhancement is done by the proposed method based on histogram.The enhanced image can be got by wavelet coefficient reconstruction.Furthermore,an evaluation criterion of enhancement performance is introduced.The results show that this algorithm ensures target enhancement and restrains additive Gauss white noise effectively.At the same time,its amount of calculation is small and operation speed is fast.展开更多
Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imag...Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.展开更多
彩色图像隐写方法具有秘密传输、不易察觉的特性。其中,基于频率域的彩色图像隐写方法不论在传统图像隐写方法还是深度学习图像隐写方法中都取得了更好的隐写性能。然而,当前大多基于自编码器结构的彩色图像隐写模型在提升重构秘密图像...彩色图像隐写方法具有秘密传输、不易察觉的特性。其中,基于频率域的彩色图像隐写方法不论在传统图像隐写方法还是深度学习图像隐写方法中都取得了更好的隐写性能。然而,当前大多基于自编码器结构的彩色图像隐写模型在提升重构秘密图像能力方面均存在局限性。针对这一问题,本文基于频率域彩色图像隐写方法的现有优势,提出了一种基于分离训练与图像去噪的频率域彩色图像隐写方法,并构建了相应的隐写模型。面对自编码器的编码网络与解码网络在训练过程中的性能权衡问题,本文的隐写方法采用分离训练对默认的神经网络训练方式进行优化。除此之外,为了进一步提升重构秘密图像的质量,模型还添加了去噪卷积神经网络(Denoising Convolutional Neural Network,DnCNN)结构的图像去噪模块。经实验验证,本文模型生成的彩色载密图像与重构秘密图像的峰值信噪比(Peak Signal to Noise Ratio,PSNR)高达82.31 dB和39.27 dB,结构相似度(Structural Similarity Index Measure,SSIM)均达到0.99。与同类型的深度学习彩色图像隐写模型相比,提出的隐写模型不仅具有更强的不可察觉性,而且具有更好的重构秘密图像的能力。展开更多
基金the Scientific Research Project of Zhejiang Education Department of China (No. Y20108569)the Soft Science Project of Ningbo of China (No. 2011A1058)the Soft Science of Zhejiang Association for Science and Technology of China (No. KX12E-10)
文摘In order to preferably identify infrared image of refuge chamber, reduce image noises of refuge chamber and retain more image details, we propose the method of combining two-dimensional discrete wavelet transform and bilateral denoising. First, the wavelet transform is adopted to decompose the image of refuge chamber, of which low frequency component remains unchanged. Then, three high-frequency components are treated by bilateral filtering, and the image is reconstructed. The result shows that the combination of bilateral filtering and wavelet transform for image denoising can better retain the details which are included in the image, while providing better visual effect. This is superior to using either bilateral filtering or wavelet transform alone. It is useful for perfecting emergency refuge system of coal mines.
基金provided by the Heilongjiang Provincial Department of Education Planning Project (No.GBC1212076)the Central University Research Project (No.00-800015Q7)
文摘Based on low illumination and a large number of mixed noises contained in coal mine, denoising with one method usually cannot achieve good results, so a multi-level image denoising method based on wavelet correlation relevant inter-scale is presented. Firstly, we used directional median filter to effectively reduce impulse noise in the spatial domain, which is the main cause of noise in mine. Secondly, we used a Wiener filtration method to mainly reduce the Gaussian noise, and then finally used a multi-wavelet transform to minimize the remaining noise of low-light images in the transform domain. This multi-level image noise reduction method combines spatial and transform domain denoising to enhance benefits, and effectively reduce impulse noise and Gaussian noise in a coal mine, while retaining good detailed image characteristics of the underground for improving quality of images with mixing noise and effective low-light environment.
文摘Deviation is essential to classic soft threshold denoising in wavelet domain. Texture features ofnoised image denoised by wavelet transform were weakened. Gibbs effect is distinct at edges of image.Image blurs comparing with original noised image. To solve the questions, a blind denoising method basedon single-wavelet transform and multiwavelets transform was proposed. The method doesn’t depend onsize of image and deviation to determine threshold of wavelet coefficients, which is different from classicalsoft-threshold denoising in wavelet domain. Moreover, the method is good for many types of noise. Gibbseffect disappeared with this method, edges of image are preserved well, and noise is smoothed andrestrained effectively.
基金the Aeronautics Science Foundation of China(20070153005)Astronautics Science Technology Innovation Foundation of China(05C53005)
文摘Aiming at the problem,i.e.infrared images own the characters of bad contrast ratio and fuzzy edges,a method to enhance the contrast of infrared image is given,which is based on stationary wavelet transform.After making stationary wavelet transform to an infrared image,denoising is done by the proposed method of double-threshold shrinkage in detail coefficient matrixes that have high noisy intensity.For the approximation coefficient matrix with low noisy intensity,enhancement is done by the proposed method based on histogram.The enhanced image can be got by wavelet coefficient reconstruction.Furthermore,an evaluation criterion of enhancement performance is introduced.The results show that this algorithm ensures target enhancement and restrains additive Gauss white noise effectively.At the same time,its amount of calculation is small and operation speed is fast.
文摘Aim To fuse the fluorescence image and transmission image of a cell into a single image containing more information than any of the individual image. Methods Image fusion technology was applied to biological cell imaging processing. It could match the images and improve the confidence and spatial resolution of the images. Using two algorithms, double thresholds algorithm and denoising algorithm based on wavelet transform,the fluorescence image and transmission image of a Cell were merged into a composite image. Results and Conclusion The position of fluorescence and the structure of cell can be displyed in the composite image. The signal-to-noise ratio of the exultant image is improved to a large extent. The algorithms are not only useful to investigate the fluorescence and transmission images, but also suitable to observing two or more fluoascent label proes in a single cell.
文摘彩色图像隐写方法具有秘密传输、不易察觉的特性。其中,基于频率域的彩色图像隐写方法不论在传统图像隐写方法还是深度学习图像隐写方法中都取得了更好的隐写性能。然而,当前大多基于自编码器结构的彩色图像隐写模型在提升重构秘密图像能力方面均存在局限性。针对这一问题,本文基于频率域彩色图像隐写方法的现有优势,提出了一种基于分离训练与图像去噪的频率域彩色图像隐写方法,并构建了相应的隐写模型。面对自编码器的编码网络与解码网络在训练过程中的性能权衡问题,本文的隐写方法采用分离训练对默认的神经网络训练方式进行优化。除此之外,为了进一步提升重构秘密图像的质量,模型还添加了去噪卷积神经网络(Denoising Convolutional Neural Network,DnCNN)结构的图像去噪模块。经实验验证,本文模型生成的彩色载密图像与重构秘密图像的峰值信噪比(Peak Signal to Noise Ratio,PSNR)高达82.31 dB和39.27 dB,结构相似度(Structural Similarity Index Measure,SSIM)均达到0.99。与同类型的深度学习彩色图像隐写模型相比,提出的隐写模型不仅具有更强的不可察觉性,而且具有更好的重构秘密图像的能力。
