Neighbourhood decision based impulse noise filter

Abstract

A novel impulse noise filter that preserves the image details and effectively suppresses high-density noise has been proposed in this work. The proposed filter works in two phases: (i) noise pixel detection phase and (ii) noise pixel restoration phase. In the detection phase, the impulse noise corrupted pixels are detected using a neighbourhood decision approach. In the second phase, the true values of corrupted pixels are restored using a first-order neighbourhood decision approach. Experiments are carried out with both grey scale and colour images of various resolutions, texture and structures. The proposed scheme has high peak-signal-to-noise ratio and better visual quality in comparison to the standard median filter, modified decision based unsymmetrical trimmed median filter and improved fast peer-group filter with a varying noise density from 10 to 90%.

Median Filtering Using First-Order and Second-Order Neighborhood Pixels to Reduce Fixed Value Impulse Noise from Grayscale Digital Images

“…Many methods were proposed to enhance SwMF. For example, in work by Samantaray et al [34], a variable threshold detector (VTD) was used, where the threshold value was dependent on the estimated noise density. The noise density was estimated by sliding an 11 × 11 mask over all the image regions.…”

“…Then, the noise density was estimated by dividing this number of extreme intensity pixels by the number of the mask's positioning over the entire image. To restore the noisy pixels, Samantaray et al [34] proposed a neighborhood-based decision filter (NBDF), where only the first-order neighborhood (FON) pixels were used to estimate the original intensity value of the noisy pixel.…”

“…The proposed image denoising stage is based on the work by Samantaray et al [34], where only FON pixels are used to estimate the original intensity value of the noisy pixel, as they are more correlated to the noisy pixel than the others. In the work by Samantaray et al [34], as some pixels left with an extreme intensity value in case of all FON pixels have extreme intensity values, the process of restoration needed to be repeated to ensure that all noisy pixels have been restored. Some pixels have extreme intensity values that remain in the second round to be restored.…”

“…However, the SON pixels are more correlated to the central pixel than the TON pixels [22]. Based on that, an enhancement is proposed to the work by Samantaray et al [34], where the SON pixels are used to estimate the original intensity value of the noisy pixels in the case of all the FON pixels having extreme intensity values. Thereafter, if all FON and SON pixels have extreme intensities, the pixel to be restored will either have an extreme intensity as the new intensity value or it should be left unchanged.…”

“…The restoration of corrupted pixels is done by estimating the original intensity value using the pixels around it. The existing restoration filters still cannot achieve high-quality restored images from corrupted images at high noise density as observed in the recent works by Chen et al [22], Samantaray et al [34], Chen et al [35] and Sangave and Jain [12]. From that, an enhancement of impulse noise restoration is required.…”