Download PDFOpen PDF in browserA Clean to Noisy Image Generation Scheme Using Generative Adversarial NetworkEasyChair Preprint 93383 pages•Date: November 18, 2022AbstractFor training, current deep learning real denoising algorithms need a lot of noisy, clean image pairings. Nevertheless, it is an extremely expensive and time-consuming process to capture a true noisy-clean dataset. To address this issue we looks towards creating realistic noisy visuals. We propose a generative adversarial network (GAN) based noise generation model which utilizes a pre-trained image denoiser to construct the fake and real noisy images into a nearly noise-free solution space. Utilizing this denoiser we have developed a network to generate realistic looking noisy images. Keyphrases: Generative Adversarial Network, Image denoising, Noisy image generation
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