Abstract
In this paper, we present an end-to-end method for image blind denoising based on a conditional generative adversarial network (GAN). Discriminative learning-based methods, such as DnCNN, can achieve state-of-the-art denoising results but these methods usually focus on establishing noise model that resembles natural noisy images, thus neglecting to recover clean images from noisy images. Non-blind denoising methods are also limited since a precise noise level is hard to be obtained in the real world. Using multiple modified methods, we propose a novel end-to-end architecture which could directly generate clean images. A range of experiments have been done to show the convenience and superiority of our approach in image blind denoising.
The first author is a student.
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Acknowledgement
This project is supported by the National Natural Science Foundation of China (61473148), \(13^{th}\) Five-Year equipment pre research project (30501050403) and GraduateInnovation Base LabOpen Fund of Nanjing University of Aeronautics and Astronautics (No. kfjj20180316).
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Zhu, S., Xu, G., Cheng, Y., Han, X., Wang, Z. (2019). BDGAN: Image Blind Denoising Using Generative Adversarial Networks. In: Lin, Z., et al. Pattern Recognition and Computer Vision. PRCV 2019. Lecture Notes in Computer Science(), vol 11858. Springer, Cham. https://doi.org/10.1007/978-3-030-31723-2_21
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DOI: https://doi.org/10.1007/978-3-030-31723-2_21
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