We propose an accurate and efficient noise detection algorithm for impulse noise removal, called the boundary discriminative noise detection by elimination (BDNDE), which retains the good characteristics of the BDND filter proposed by Ng and Ma (in IEEE Trans. Image Process. 15(6):1506–1516, 2006) while suppressing noise effectively. In order to determine whether a pixel is corrupted, the algorithm first sets the minimum and maximum boundary (threshold) values based on the localized window centered on the pixel. The thresholding helps in achieving low false-alarm and miss-detection rate (even in random noise), even up to 90% noise densities. Extensive simulation results, conducted on gray scale images under a wide range (from 10 to 90%) of noise corruption, clearly demonstrate that our enhanced switching median filter gives better results compared to existing BDND median-based filters, in terms of suppressing impulse noise while preserving image details. The proposed method is algorithmically simple and faster, compared to existing BDND, and more suitable for real-time implementation and application. The new method has shown superior performance in terms of subjective quality in the filtered image as well as objective quality in the peak signal-to-noise ratio (PSNR) measurement to that of the BDND filter.
Image denoisingImpulse noise detectionNonlinear filterSwitching median filter