Improvement of quantized adaptive switching median filter for impulse noise reduction in gray-scale digital images

Abstract

Digital images may suffer from fixed value impulse noise due to several causes. The noise significantly degradesthe quality of the image, which may affect the subsequence image processing. Therefore, a noise reduction technique isrequired to restore the image. In this paper, a new method, which is called improvement of quantized adaptive switchingmedian filter (IQASMF), has been proposed to reduce the fixed value impulse noise from gray-scale digital images. Theimplementation of IQASMF has five processing blocks. The first processing block is the noise detection block, where thenoise pixel candidates are detected based on the intensity value. Then estimation of the local noise density is done by thesecond processing block. Next, the third processing block filters the corrupted pixel candidates with filters of predefinedsize, depending on the local noise density. After that, the noise mask is updated in the fourth processing block. Finally,the fifth processing block processes the noise residuals from the third processing block by using a size adaptive filter.Experimental results from twenty standard gray-scale images of various sizes have shown that IQASMF has the abilityto restore images for up to 99% of the impulse noise corruption. As compared with the other five median filter-basedmethods, from the measures of mean squared error (MSE) and structural similarity index (SSIM), it is shown that theperformance of IQASMF is equivalent to the performance of other methods at low and medium levels of corruption.However, at high corruption levels, IQASMF has demonstrated the best performance in terms of MSE and SSIM. Theoutputs from IQASMF also have the best visual appearance.