Published on Feb 16, 2016
A novel noise adaptive soft-switching median filter is proposed in this thesis titled "An adaptive soft-switching median filter for impulse noise removal". It contains a switching mechanism steered by a soft-switching noise-detection scheme to identify each pixel's characteristic, followed by invoking proper filtering operation.
In the noise-detection scheme, global (i.e., based on the entire picture) or local (i.e., based on a small window) pixel statistics are utilized in the first and the remaining two decision-making levels, respectively.
Most of the true pixels are successfully identified as uncorrupted pixels in the first decision-making level. Other remaining unidentified pixels will be further discriminated in the remaining two decision levels as isolated impulse noise, non-isolated impulse noise or edge pixel. The concept of fuzzy logic is exploited in the latter stage to achieve soft switching. In the filtering scheme, action of no filtering (identity filter) is applied to those identified uncorrupted pixels .
SM or a fuzzy weighted median (FWM) filter would be subsequently carried out to remove impulse noise or preserve image object's edge details, depending on the pixel's characteristic identified. Instead of exploiting no filtering to the edge pixels, the proposed FWM is developed to effectively compensate possible degraded performance due to misclassification of non-isolated impulse noise as edge pixel.
FWM is essentially an adaptive WM in which larger weights are assigned to more correlated pixels, based on the local statistics of pixel intensity. By doing so, FWM incorporates the pixel-intensity correlation to enhance its filtering capability in attenuating impulse noise while preserving image details.
Noise can occur during image capture, transmission or processing, and may be dependent on or independent of image content. Images are often degraded by random noise. Noise is usually described by its probabilistic characteristics. White noise is a constant power spectrum (its intensity does not decrease with increasing frequency) with very crude approximation of image noise where as Gaussian noise is an approximation of noise that occurs in many practical cases
Conventional switching based median filters make use of a fixed noise-detection threshold obtained at a pre-assumed noise density level and hence lack of adaptivity to noise density variation.
The mismatch between the designed algorithms and the actual noise density, which is often unknown in priori, will cause noticeable and even substantial degradation on filtering performance.
Second, when the noise density increases, more misclassifications of pixel characteristic are going to occur and subsequently result in more degraded filtering performance.
Therefore, an intelligent noise-detection process will be highly desirable and instrumental in correctly detecting various types of pixel characteristic. In addition, an adaptive filtering scheme is essential to effectively remove the corrupted pixels while preserving image details when misclassification of pixel characteristic happens. These indicate that both noise detection and corresponding filtering operation are crucial to achieve good median filtering performance, especially at high noise density interference.