Denoising hybrid noises in image with stacked autoencoder

A method based on sparse denoising autoencoder for denoising hybrid noises in image is proposed in this paper. The method is experimented on natural images and the performance is evaluated in terms of peak signal to noise ratio (PSNR). By specifically designing the training process of sparse denoisi...

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Veröffentlicht in:2015 IEEE International Conference on Information and Automation S. 2720 - 2724
Hauptverfasser: Xiufen Ye, Lin Wang, Huiming Xing, Le Huang
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2015
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Abstract A method based on sparse denoising autoencoder for denoising hybrid noises in image is proposed in this paper. The method is experimented on natural images and the performance is evaluated in terms of peak signal to noise ratio (PSNR). By specifically designing the training process of sparse denoising autoencoder, our model not only achieves good performance on single kind of noises, but also is relatively robust to mixed noises, which are more widely existed in practical situation. Autoencoder is a major branch of deep learning. It has been used in many applications as the method to exact features for its ability to represent the input data. Applying autoencoder to image denoising has been achieved good performance. Further research was deployed to find that autoencoder method is relatively robust compared with BM3D. And a sparse denoising autoencoder model is employed to train the network and it works well for the hybrid noise situation.
AbstractList A method based on sparse denoising autoencoder for denoising hybrid noises in image is proposed in this paper. The method is experimented on natural images and the performance is evaluated in terms of peak signal to noise ratio (PSNR). By specifically designing the training process of sparse denoising autoencoder, our model not only achieves good performance on single kind of noises, but also is relatively robust to mixed noises, which are more widely existed in practical situation. Autoencoder is a major branch of deep learning. It has been used in many applications as the method to exact features for its ability to represent the input data. Applying autoencoder to image denoising has been achieved good performance. Further research was deployed to find that autoencoder method is relatively robust compared with BM3D. And a sparse denoising autoencoder model is employed to train the network and it works well for the hybrid noise situation.
Author Le Huang
Lin Wang
Xiufen Ye
Huiming Xing
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  organization: Coll. of Autom., Harbin Eng. Univ., Harbin, China
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  surname: Lin Wang
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  organization: Coll. of Autom., Harbin Eng. Univ., Harbin, China
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  surname: Huiming Xing
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  organization: Coll. of Autom., Harbin Eng. Univ., Harbin, China
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  surname: Le Huang
  fullname: Le Huang
  organization: Coll. of Autom., Harbin Eng. Univ., Harbin, China
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Snippet A method based on sparse denoising autoencoder for denoising hybrid noises in image is proposed in this paper. The method is experimented on natural images and...
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SubjectTerms deep learning
Image denoising
Neural networks
Noise
Noise reduction
Speckle
stacked sparse denoising autoencoder
Testing
Training
Title Denoising hybrid noises in image with stacked autoencoder
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