AT-2FF: Adaptive Type-2 Fuzzy Filter for De-noising Images Corrupted with Salt-and-Pepper
This is an incremental improvement for image processing applications requiring noise reduction.
The paper tackles the problem of removing salt-and-pepper noise from digital images while preserving features like edges and corners, resulting in a filter that outperforms other methods in simulation tests.
Noise is inevitably common in digital images, leading to visual image deterioration. Therefore, a suitable filtering method is required to lessen the noise while preserving the image features (edges, corners, etc.). This paper presents the efficient type-2 fuzzy weighted mean filter with an adaptive threshold to remove the SAP noise. The present filter has two primary steps: The first stage categorizes images as lightly, medium, and heavily corrupted based on an adaptive threshold by comparing the M-ALD of processed pixels with the upper and lower MF of the type-2 fuzzy identifier. The second stage eliminates corrupted pixels by computing the appropriate weight using GMF with the mean and variance of the uncorrupted pixels in the filter window. Simulation results vividly show that the obtained denoised images preserve image features, i.e., edges, corners, and other sharp structures, compared with different filtering methods.