Edge-based Bilateral Filter for Restoring Real Noisy Image

Abstract

This paper presents an efficient edge-based bilateral filter for real noisy image restoration. By dividing all pixels of a noisy image into edge region or non-edge region, the different strategies and parameters are adopted in the edge based bilateral filter to balance the conservation of image features and the reduction of noise level. Extensive experimental results are shown that this filter achieves very competitive performance in restoring real noisy images, compared with other state-of-the-art de-noising algorithms.  First estimates a “fuzzy derivative” in order to be less sensitive to local variations due to image structures such as edges  Second, the membership functions are adapted accordingly to the noise level to perform “fuzzy smoothing.”  For each pixel that is processed, the first stage computes a fuzzy derivative. Second, a set of 16 fuzzy rules is fired to determine a correction term. These rules make use of the fuzzy derivative as input.  Fuzzy sets are employed to represent the properties, while the membership functions for and are fixed, the membership function for is adapted after each iteration.

Existing System

Proposed System

Architecture