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LFAD: Locally- and Feature-Adaptive Diffusion based Image Denoising |
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PP: 1-12 |
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Author(s) |
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Ajay K. Mandava,
Emma E. Regentova,
George Bebis,
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Abstract |
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| LFAD is a novel locally- and feature-adaptive diffusion based method for removing additive white Gaussian (AWG) noise
in images. The method approaches each image region individually and uses a different number of diffusion iterations per region to
attain the best objective quality according to the PSNR metric. Unlike block-transform based methods, which perform with a predetermined
block size, and clustering-based denoising methods, which use a fixed number of classes, our method searches for an
optimum patch size through an iterative diffusion process. It is initialized with a small patch size and proceeds with aggregated (i.e.,
merged) patches until the best PSNR value is attained. Then the diffusion model is modified; instead of the gradient value, we use the
inverse difference moment (IDM), which is a robust feature in determining the amount of local intensity variation in the presence of
noise. Experiments with benchmark images and various noise levels show that the designed LFAD outperforms advanced diffusionbased
denoising methods, and it is competitive with state-of-the-art block-transformed techniques; block and ring artifacts inherent to
transform-based methods are reduced while PSNR levels are comparable. |
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