Abstract
Image denoising is an important preprocessing step in image analysis. The image denoising models based on deep learning have been recently attracting considerable attention owing to its excellent performance. However, most existing methods are only suitable for certain types of noise, and these methods often cause the missing of detail information in images. In this paper, an image denoising method with generative adversarial network is proposed. In this framework, a conditional GAN with an embedment of residual dense blocks (RDBs) in the generator is used for image denoising, in which the images with some artificial noises are used as condition. Meanwhile, a sum of Wasserstein distance, perceptual loss and reconstruction loss is employed to act as our final objective loss function to train the model. By comparing with several experiments with different denoising models on several datasets, the generated results show that our proposed model can reduce the noise and recover the noise images effectively.
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Acknowledgements
This work is supported by the Science and Technology Development Program of Jilin Province, China (Nos. 20170204031GX, 20180201037SF, 20190201196JC and 20190302112GX) and the Science and Technology Development Program of Jilin Province, China (No. 2018C039-1).
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Li, Y., Zhang, C., Jiang, Z. (2021). Image Denoising Using a Conditional Generative Adversarial Network with Residual Dense Blocks. In: Pan, JS., Li, J., Ryu, K.H., Meng, Z., Klasnja-Milicevic, A. (eds) Advances in Intelligent Information Hiding and Multimedia Signal Processing. Smart Innovation, Systems and Technologies, vol 212. Springer, Singapore. https://doi.org/10.1007/978-981-33-6757-9_55
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