SUPPRESSING OF IMAGE DIGITAL NOISE USING A NEURAL NETWORK BASED ON U-NET

Anton A. Kuznetsov

Abstract


A digital image is a computer representation of an optical image. The process of obtaining a digital image using digital cameras is always accompanied by noise. Removing noise from an image is an important stage in digital image processing, since noise at large values degrades the quality of the image and complicates subsequent analysis of the data on it. Noise in the image can occur due to environmental factors, ISO sensitivity, camera sensor and so on. The purpose of this research is to create a method for improving the visual quality of images by reducing the noise presented in them. This method, based on neural networks, will work with RAW images, converting them into RGB images. The resulting RGB image will be noise-free.
The performance of the presented technique is evaluated in terms of noise reduction and image detail preservation. Experimental results demonstrate the effectiveness of the proposed denoising method in achieving significant noise reduction while maintaining image details.


Keywords


RAW Images; U-Net; image denoise

Full Text:

PDF

References


De Haan G., Kwaaitaal-Spassova G., Larragy M., Ojo O., Schutten R. Television Noise Reduction IC. IEEE Transactions on Consumer Electronics, 1998, vol. 44, no. 1, pp. 143-154. DOI: 10.1109/30.663741.

Devies A., Fennessy P. Digital Imaging for Photographers. Focal Press, Waltham, 2001.

Surin V.A. About Processing Noisy Contrast Images. Bulletin of South Ural State University. Series: Mathematics. Mechanics. Physics, 2021, vol. 13, pp. 14-21. DOI: 10.14529/mmph210102 (in Russian)

U-Net: Convolutional Networks for Biomedical Image Segmentation. Available online: https://arxiv.org/abs/1505.04597 (accessed on 28 November 2023)

Gilyazetdinov E.V., Konovalov B.D. Removing Noise from Images Using Neural Networks. E-Scio, 2019, vol. 6, pp. 367-377. (in Russian)

Huang G., Liu Z., Maaten L., Weinberger K. Densely Connected Convolutional Networks. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017, pp. 2261-2269. DOI: 10.1109/CVPR.2017.243

Srivastava N., Hinton, G., Krizhevsky A., Sutskever I., Salakhutdinov R. Dropout: A Simple Way to Prevent Neural Networks from Overfitting. Journal Of Machine Learning Research, 2014, vol. 15, pp. 1929-1958.

Boukhayma A., Caizzone A., Enz C. A CMOS Image Sensor Pixel Combining Deep Sub-Electron Noise with Wide Dynamic Range. IEEE Electron Device Letters, 2020, vol. 41, no. 6, pp. 880-883. DOI: 10.1109/LED.2020.2988378.

Garcia G.B., Suarez O.D., Aranda J.L.E., Tercero J.S., Gracia I.S., Enano N.V. Learning Image Processing Using OpenCV. Packt Publishing, Birmingham, Mumbai, 2015.


Refbacks

  • There are currently no refbacks.


 Save