A new nonlinear algorithm for the removal of impulse noise from highly corrupted images

Images are often corrupted by impulse noise from a noisy sensor or channel transmission errors. The goal of impulse noise removal is to suppress the noise while preserving the edges and the details. To this end, nonlinear techniques have been found to provide more satisfactory results in comparison to linear methods. The two often used nonlinear methods are the median filter and the outlier method. In median filtering the central pixel inside a window is replaced by the median value of all pixels in the window. However, median filtering not only removes impulse noise but also introduces distortion, in particular, for highly corrupted images. The outlier method is based on a detection-estimation strategy in which the impulse noise is detected first and then an average of all pixel values inside a window centered at the defected corrupted pixel is used as an estimate of its true value. This approach is also not effective for highly corrupted images. Recently, we proposed an efficient method to remove impulse noise from a highly corrupted image also based on a detection-estimation strategy, in which the impulse noise is detected first using a simple quadratic filter, and a selectively chosen local mean is used to estimate the true value of the corrupted pixel. A major limitation of this approach is that often it has to be applied twice to arrive at a satisfactory result. This second run not only increases computational cost but also reduce the quality of the processed image. In this paper, we analyze the limitation of this newly proposed method of impulse noise removal, and then advance a modified version which does not require a second run