• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Estimation of Noise Level in Complex Textured Images and Monte Carlo-Rendered Images

Vol. 10, No. 1, January 30, 2016
10.3837/tiis.2016.01.022, Download Paper (Free):

Abstract

The several noise level estimation algorithms that have been developed for use in image processing and computer graphics generally exhibit good performance. However, there are certain special types of noisy images that such algorithms are not suitable for. It is particularly still a challenge to use the algorithms to estimate the noise levels of complex textured photographic images because of the inhomogeneity of the original scenes. Similarly, it is difficult to apply most conventional noise level estimation algorithms to images rendered by the Monte Carlo (MC) method owing to the spatial variation of the noise in such images. This paper proposes a novel noise level estimation method based on histogram modification, and which can be used for more accurate estimation of the noise levels in both complex textured images and MC-rendered images. The proposed method has good performance, is simple to implement, and can be efficiently used in various image-based and graphic applications ranging from smartphone camera noise removal to game background rendition.


Statistics

Show / Hide Statistics

Statistics (Cumulative Counts from December 1st, 2015)
Multiple requests among the same browser session are counted as one view.
If you mouse over a chart, the values of data points will be shown.


Cite this article

[IEEE Style]
I. Kim, "Estimation of Noise Level in Complex Textured Images and Monte Carlo-Rendered Images," KSII Transactions on Internet and Information Systems, vol. 10, no. 1, pp. 381-394, 2016. DOI: 10.3837/tiis.2016.01.022.

[ACM Style]
I-Gil Kim. 2016. Estimation of Noise Level in Complex Textured Images and Monte Carlo-Rendered Images. KSII Transactions on Internet and Information Systems, 10, 1, (2016), 381-394. DOI: 10.3837/tiis.2016.01.022.