11-02-2013, 03:27 PM
ADAPTIVE NOISE REDUCTION SCHEME FOR SALT
AND PEPPER
ADAPTIVE NOISE REDUCTION.pdf (Size: 468.58 KB / Downloads: 59)
INTRODUCTION
The two most common types of noise in image processing are Gaussian noise and Impulse
noise, also known as salt and pepper noise. This type of noise may appear in digital image due
contaminated impulse noise, which is caused by malfunctioning pixels in camera sensors, faulty
memory location in hardware, or transmission in noisy channel [9]. Salt and pepper noise
scattered throughout the image in such a way pixels can take only the maximum and minimum
values (0 and 255 respectively) in the dynamic range.
Many researches have been conducted and numerous algorithms were proposed to remove salt
and pepper noise. Among these noise reduction techniques, majority splits the noise removal
procedures into preliminarily detection of pixels corrupted by impulse noise followed by
filtering the noise detected on the previous phase [8]. Standard median filter (SMF) [7] was one
of famous among others due to its great denoising performance and computational efficiency.
But since the conventional median filter applies the median operation to each pixel whether it is
corrupted or not, it suffers from preserving some details of the image as the noise density
increases. More improved algorithms such as adaptive median filter (AMF) [2], decision-based
algorithm (DBA) [1] and convolution-based algorithm (CBA) [3] mainly focus on noise
detector. Pixels detected by the noise detector will be considered as noise and shall further be
processed in their respective noise reduction scheme. Having such mechanism with phases
would highly preserve the details of the image and save the restored image from having blurred
and distorted feature.
The proposed algorithm in this paper also greatly focuses on how to effectively detect the salt
and pepper noise and efficiently restore the image. The mechanism adopted by the proposed
scheme consists of three phases. The first phase determines whether a pixel is noise or not based
on some predefined threshold and calculated values. Once pixels are detected as noise in
previous phase, their new value will be estimated and set in noise reduction phase. Finally a
conditional image enhancement phase will be conducted for those images which have been
corrupted with high density noise to preserve edges and details of the restored image. This
makes the proposed algorithm to have an outstanding performance even at noise density as high
as 90%.
The rest of this paper is organized as follows. Section 2 describes the proposed scheme. Section
3 presents the experimental results. And the last section, Section 4 concludes this paper.
CONCLUSION
In this paper, a new adaptive noise reduction scheme for removing salt and pepper noise is
proposed. The first phase of the scheme efficiently identifies impulse noise while the other is to
remove the noise from the corrupted image that is followed by image enhancement scheme to
preserve the details and image quality. As per the experimental results, the proposed algorithm
yields good filtering result using efficient noise detection mechanism. This is observed by
numerical measurements like PSNR and visual observations through the experiments
conducted.