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Image Reconstruction of Noisy Distorted Images Using Deep Learning Methods: A Review

Anamika Tiwari, Prof. Silky Pareyani

Abstract


Abstract

The field of digital image processing deals not only with the extraction of features and analysis of images but also restoration of images. Image restoration is one of the basic steps of processing that deals with making certain improvements in a digital image based on some predefined criteria. A deep learning architecture for image restoration that attains statistically significant improvements over traditional algorithms is Poisson image de-noising, especially when the noise is strong. Poisson noise commonly occurs in low-light and photon-limited settings, where the noise can be most accurately modeled by the Poisson distribution.


Keywords


Image reconstruction, deep learning, convolutional neural network, image de-noising, additive white Gaussian noise

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