Autoencoders Based Deep Learner for Image Denoising
Nowadays, digital images have a valuable role in our daily life, and can be used for various of applications like fingerprint recognition, video surveillance etc. Sometimes, images get infected with noise due to many reasons such as defects in camera sensors, transmission in noisy channel, faulty me...
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| Vydáno v: | Procedia computer science Ročník 171; s. 1535 - 1541 |
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| Jazyk: | angličtina |
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Elsevier B.V
2020
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| ISSN: | 1877-0509, 1877-0509 |
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| Abstract | Nowadays, digital images have a valuable role in our daily life, and can be used for various of applications like fingerprint recognition, video surveillance etc. Sometimes, images get infected with noise due to many reasons such as defects in camera sensors, transmission in noisy channel, faulty memory locations in the hardware etc. Processing a noisy image is not advisable because usually it yields erroneous outcomes. So, as to improve it for subsequence processing, the noise must be eliminated from the image in advance. Therefore, there is a need of an efficient image denoising technique that helps to deal with noisy image. Image de-noising is a process to realign the original image from the degraded image. In this paper, autoencoders based deep learning model is proposed for image denoising. The autoencoders learns noise from the training images and then try to eliminate the noise for novel image. The experimental outcomes prove that this proposed model for PSNR has achieved higher result compared to the conventional models. |
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| AbstractList | Nowadays, digital images have a valuable role in our daily life, and can be used for various of applications like fingerprint recognition, video surveillance etc. Sometimes, images get infected with noise due to many reasons such as defects in camera sensors, transmission in noisy channel, faulty memory locations in the hardware etc. Processing a noisy image is not advisable because usually it yields erroneous outcomes. So, as to improve it for subsequence processing, the noise must be eliminated from the image in advance. Therefore, there is a need of an efficient image denoising technique that helps to deal with noisy image. Image de-noising is a process to realign the original image from the degraded image. In this paper, autoencoders based deep learning model is proposed for image denoising. The autoencoders learns noise from the training images and then try to eliminate the noise for novel image. The experimental outcomes prove that this proposed model for PSNR has achieved higher result compared to the conventional models. |
| Author | Bajaj, Komal Singh, Dushyant Kumar Ansari, Mohd. Aquib |
| Author_xml | – sequence: 1 givenname: Komal surname: Bajaj fullname: Bajaj, Komal organization: Department of Computer Science & Engineering, MNNIT Allahabad, Prayagraj, U.P., INDIA – sequence: 2 givenname: Dushyant Kumar surname: Singh fullname: Singh, Dushyant Kumar email: dushyant@mnnit.ac.in organization: Department of Computer Science & Engineering, MNNIT Allahabad, Prayagraj, U.P., INDIA – sequence: 3 givenname: Mohd. Aquib surname: Ansari fullname: Ansari, Mohd. Aquib organization: Department of Computer Science & Engineering, MNNIT Allahabad, Prayagraj, U.P., INDIA |
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| Keywords | Deep learning Convolutional Neural Network Noise Autoencoders Denoising |
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| SubjectTerms | Autoencoders Convolutional Neural Network Deep learning Denoising Noise |
| Title | Autoencoders Based Deep Learner for Image Denoising |
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