Abstract
This paper proposes an adaptive noise detector and a new weighted mean filter to remove random-valued impulse noise from the images. Unlike other noise detectors, the proposed detector computes a new and adaptive threshold for each pixel. The detection accuracy is further improved by employing edge identification stage to ensure that the edge pixels are not incorrectly detected as noisy pixels. Thus, preserving the edges avoids faulty detection of noise. In the filtering stage, a new weighted mean filter is designed to filter only those pixels which are identified as noisy in the first stage. Different from other filters, the proposed filter divides the pixels into clusters of noisy and clean pixels and thus takes into only clean pixels to find the replacement of the noisy pixel. Simulation results show that the proposed method outperforms state-of-the-art noise detection methods in suppressing random valued impulse noise.
Highlights
For image analysis, de-noising is an important pre-processing step
Digital images are oftentimes corrupted by impulse noise during acquisition, transmission and impaired camera sensors [1], which degrades image features such as edges, sharpness, depth, etc
Impulse noise is of two types: Salt & Pepper Noise (SPN) and Random Valued Impulse Noise (RVIN) [2]
Summary
De-noising is an important pre-processing step. Digital images are oftentimes corrupted by impulse noise during acquisition, transmission and impaired camera sensors [1], which degrades image features such as edges, sharpness, depth, etc. Median Filter (RWMF) [10], the Multi-state Median Filter [11] the Central Weighted Median Filter (CWMF) [12], the Rank-Order Mean Filter and the Stack Filter [13] These filters still degrade the quality of images as they replace all pixels in the image without considering whether the test pixel is noisy or not [14]. Once the noise is detected, the noisy pixel is replaced by weighted median value in the optimal direction. We propose an adaptive noise detector and a new weighted mean filter to remove random valued impulse noise from the images. Extensive simulation results are presented that show that the proposed RVIN suppression mechanism is superior compared to the state-of-the-art schemes
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