Denoising is the task of removing noise from an image.
( Image credit: Beyond a Gaussian Denoiser )
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We propose an in silico molecular associative memory model for pattern learning, storage and denoising using Pairwise Markov Random Field (PMRF) model.
Through the comprehensive experimental results and analysis, this paper presents the inherent property of adversarial robustness in the autoencoders.
All of this helps conclude that, thanks to alterations in their structure as well as their objective function, autoencoders may be the core of a possible solution to many problems which can be modeled as a transformation of the feature space.
Extensive experiments demonstrate the superiority of the proposed network in terms of suppressing the chromatic aberration and noise artifacts in enhancement, especially when the low-light image has severe noise.
The de facto training protocol to achieve this goal is to train the estimator with noisy samples whose noise levels are uniformly distributed across the range of interest.
It is necessary to reduce the dose of CTP for routine applications due to the high radiation exposure from the repeated scans, where image denoising is necessary to achieve a reliable diagnosis.
Total Generalized Variation (TGV) has recently been introduced as penalty functional for modelling images with edges as well as smooth variations.