Single Image Based Random-Value Impulse Noise Level Estimation Algorithm.
Bao, Long.
Panetta, Karen.
Agaian, S. S.
2019
- Image de-nosing is a vital and indispensable pre-process for most applied image processing systems. Having prior knowledge about the noise level is essential for optimizing de-nosing algorithms. However, this information most likely doesn't exist for real applications and is much harder to extract from a single noisy image than from multiple noisy images. For Gaussian noise, there are many accurate ... read morestate-of-the-art level estimations, whereas there are only limited random valued impulse noise level estimations proposed. Moreover, the existing proposed impulse noise estimators are limited in accuracy, especially in the presence of high noise levels. This paper presents a new random-valued impulse noise level estimation (RVI-E) algorithm using only a single image. The presented RVI-E algorithm is based on distribution property of impulse noise pixels, on correlation among the image, and on a new linear relationship between the percentage of big-distorted noise and one of all noise. The mathematical study, computer simulations, and analysis on 347 different images using five online grayscale image databases shows that a) the presented method is efficient, robust and reliable, b) the presented methods show stably accurate performance across images with different contents and different levels of noise (lower than 60%), c) the speed performance of the proposed RVI can be boosted by the parallel computing strategy, since the estimation utilizes a parallel framework. This is a post-peer-review, pre-copyedit version of a paper published in Advances in Intelligent Systems and Computing. The final authenticated version is available online at: https://doi.org/10.1007/978-3-030-01177-2_95.read less
- Bao L., Panetta K., Agaian S. (2019) Single Image Based Random-Value Impulse Noise Level Estimation Algorithm. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Computing. SAI 2018. Advances in Intelligent Systems and Computing, vol 857. Springer, Cham. https://doi.org/10.1007/978-3-030-01177-2_95.
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