Real-Time Impulse Noise Suppression from Images Using an Efficient Weighted-Average Filtering
This addresses the need for efficient noise removal in image processing applications, though it appears incremental as it builds on existing filtering techniques.
The paper tackles the problem of real-time high-density impulse noise suppression in images by proposing a method that uses an impulse detector and a weighted-average filter, achieving higher PSNR and better visual quality than existing methods.
In this paper, we propose a method for real-time high density impulse noise suppression from images. In our method, we first apply an impulse detector to identify the corrupted pixels and then employ an innovative weighted-average filter to restore them. The filter takes the nearest neighboring interpolated image as the initial image and computes the weights according to the relative positions of the corrupted and uncorrupted pixels. Experimental results show that the proposed method outperforms the best existing methods in both PSNR measure and visual quality and is quite suitable for real-time applications.