Abstract
In most of the real world imaging applications like synthetic aperture radar (SAR), microscope and laser images, multiplicative noise is more prevalent than the additive Gaussian noise. In our present work, we have focused on multiplicative noise removal models. Standard diffusion based multiplicative Gamma noise removal models produce highly smoothed images. In addition, their restoration performance mainly depends on the proper choice of the built-in parameters and the diffusion coefficient. In this paper, we study various learning frameworks, including our own, regarding the applicability of the models in case of denoising of highly corrupted images. We show through numerical experiments that the considered trainable models perform better than the state-of-the-art PDE models in terms of peak-signal-to-noise ratio (PSNR).
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Jetta, M., Singh, U., Yinukula, P. (2023). On Trainable Multiplicative Noise Removal Models. In: Calatroni, L., Donatelli, M., Morigi, S., Prato, M., Santacesaria, M. (eds) Scale Space and Variational Methods in Computer Vision. SSVM 2023. Lecture Notes in Computer Science, vol 14009. Springer, Cham. https://doi.org/10.1007/978-3-031-31975-4_7
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