Abstract
In the process of image generation or transmission, the image quality is often degraded due to the interference and influence of speckle noise, which will adversely affect the subsequent image processing. However, most of the existing Optical Coherence Tomography (OCT) image denoising methods usually only use part of the prior information of the OCT image, and ignore the changes in the texture and structure of the OCT image. There is often a problem that the network is too deep and the calculation complexity is too large. Aiming at this shortcoming, this paper proposes an improved deep convolutional neural network image denoising algorithm based on the original DenseNet algorithm idea. First, by constructing a speckle noise image data set, a series of preprocessing is performed on the input image data set, then the visual statistical feature map of the image is extracted, and finally the DenseNet network structure is improved for network training. The experimental results show that the improved DenseNet has better performance no matter compared with BM3D, which is recognized as the best denoising algorithm in the field of image denoising, or compared with DnCNN, an advanced image denoising algorithm in the field of deep learning.
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References
Dang, N., Prasath, S., Nguyen, V.S., Minh, H.L.: An adaptive image inpainting method based on the modified Mumford-Shah model and multiscale parameter estimation. Comput. Opt. 43(2), 251–257 (2019)
Yao, X., Ji, K., Liu, G., Shi, W., Gao, W.: Blood flow imaging by optical coherence tomography based on speckle variance and doppler algorithm. Laser Optoelectron. Prog. 54(3), 031702 (2017)
Zhang, J., Ding, S., Zhang, N.: An overview on probability undirected graphs and their applications in image processing. Neurocomputing 321, 156–168 (2018)
Fang, L., Li, S., Cunefare, D., Farsiu, S.: Segmentation based sparse reconstruction of optical coherence tomography images. IEEE Trans. Med. Imaging (2017)
Xing, Y., Xu, J., Tan, J., Li, D., Zha, W.: Deep CNN for removal of salt and pepper noise. Image Process. IET (2019)
Karibasappa, K.G., Karibasappa, K.: AI based automated identification and estimation of noise in digital images. In: El-Alfy, E.-S.M., Thampi, S.M., Takagi, H., Piramuthu, S., Hanne, T. (eds.) Advances in Intelligent Informatics. AISC, vol. 320, pp. 49–60. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-11218-3_6
Chang, X., Shi, W., Zhang, F.: Signed network embedding based on noise contrastive estimation and deep learning. In: Ni, W., Wang, X., Song, W., Li, Y. (eds.) Web Information Systems and Applications, pp. 40–46. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-30952-7_5
Lin, C., Li, Y., Feng, S., Huang, M.: A two-stage algorithm for the detection and removal of random-valued impulse noise based on local similarity. IEEE Access 99, 1 (2020)
Huo, F., Zhang, W., Wang, Q., Ren, W.: Two-stage image denoising algorithm based on noise localization. Multimedia Tools Appl. 2 (2021)
Vasuki, P., Bhavana, C., Roomi, S., Deebikaa, E.L.: Automatic noise identification in images using moments and neural network. In: International Conference on Machine Vision & Image Processing (2013)
Amini, Z., Rabbani, H.: Optical coherence tomography image denoising using Gaussianization transform. J. Biomed. Opt. 22 (2017)
Fu, B., Zhao, X., Li, Y., Wang, X., Ren, Y.: A convolutional neural networks denoising approach for salt and pepper noise. Multimedia Tools Appl. 78(21), 30707–30721 (2018). https://doi.org/10.1007/s11042-018-6521-4
Acknowledgements
Supported by Hainan Provincial Natural Science Foundation of China(Grant #: 2019CXTD400), National Key Research and Development Program of China (Grant #: 2018YFB1404400), National Natural Science Foundation of China(Grant #: 62062030, Major Science and Technology Project of Haikou (Grant #: 2020-009), Project supported by the Education Department of Hainan Province (Grant #: Hnky2019-22).
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Huang, M. et al. (2021). Image Noise Recognition Algorithm Based on Improved DenseNet. In: Xing, C., Fu, X., Zhang, Y., Zhang, G., Borjigin, C. (eds) Web Information Systems and Applications. WISA 2021. Lecture Notes in Computer Science(), vol 12999. Springer, Cham. https://doi.org/10.1007/978-3-030-87571-8_39
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DOI: https://doi.org/10.1007/978-3-030-87571-8_39
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