Abstract
In Poisson noise, the number of collected photons is so small due to the low light environment that degrades the image by reducing image resolution and contrast. Therefore, Poisson noise evaluation is necessary for the restoration of digital images. In this chapter, a filter based on the partial differential equation is introduced to restore noisy images. The proposed PDE-based filter describes Poisson noise-adapted anisotropic diffusion-based method in the L-2 framework. Regularization function and data fidelity are two components of this filter. A regularization parameter lambda is inserted throughout the filtering process to ensure proper stability between the data fidelity and the regularization function. Evaluation of performance parameters for all the described techniques is done with the help of MSE and PSNR.
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Singh, N., Yadav, R.B. (2022). De-Noising of Poisson Noise Corrupted CT Images by Using Modified Anisotropic Diffusion-Based PDE Filter. In: Kumar, N., Shahnaz, C., Kumar, K., Abed Mohammed, M., Raw, R.S. (eds) Advance Concepts of Image Processing and Pattern Recognition. Transactions on Computer Systems and Networks. Springer, Singapore. https://doi.org/10.1007/978-981-16-9324-3_7
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DOI: https://doi.org/10.1007/978-981-16-9324-3_7
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