Abstract
This paper describes two methods for impulse noise reduction in colour images that outperform the vector median filter from the noise reduction capability point of view. Both methods work by determining first the vector median in a given filtering window. Then, the use of complimentary information from componentwise analysis allows to build robust outputs from more reliable components. The correlation among the colour channels is taken into account in the processing and, as a result, a more robust filter able to process colour images without introducing colour artifacts is obtained. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter. Objective measures demonstrate the goodness of the achieved improvement.
Highlights
Image denoising has been for years a very active research topic within image processing because of its necessity for most computer vision systems
Experimental results show that the images filtered with the proposed methods contain less noisy pixels than those obtained through the vector median filter
For Modified Vector Median Filter (MVMF) and Robust Vector Median Filter (RVMF) we have set t = 3 because this is the smallest value that makes sense to use and because increasing this value will decrease the reliability of the method from the artifact generation point of view
Summary
Image denoising has been for years a very active research topic within image processing because of its necessity for most computer vision systems. The process of denoising or filtering a signal consists of transforming an input signal into another more suitable one for a given purpose. This means to reduce as much as possible the contaminating noise. Several types of noise have been studied in the literature. They may appear alone or be mixed in the digital images. In this work we focus on the impulse noise case, which affect a number of pixels in the image by replacing its original values with other very different ones
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