TITLE:
Metaheuristic Based Noise Identification and Image Denoising Using Adaptive Block Selection Based Filtering
AUTHORS:
M. Sasikala Devi, R. Sukumar
KEYWORDS:
Adaptive Block Selection, Enhancement Filtering, Image Denoising, Noise Identification, Particle Swarm Optimization
JOURNAL NAME:
Circuits and Systems,
Vol.7 No.9,
July
29,
2016
ABSTRACT: Image denoising has become
one of the major forms of image enhancement methods that form the basis of
image processing. Due to the inconsistencies in the machinery producing these
signals, medical images tend to require these techniques. In real time, images
do not contain a single noise, and instead they contain multiple types of noise
distributions in several indistinct regions. This paper presents an image
denoising method that uses Metaheuristics to perform noise identification.
Adaptive block selection is used to identify and correct the noise contained in
these blocks. Though the system uses a block selection scheme, modifications
are performed on pixel- to-pixel basis and not on the entire blocks; hence the
image accuracy is preserved. PSO is used to identify the noise distribution,
and appropriate noise correction techniques are applied to denoise the images.
Experiments were conducted using salt and pepper noise, Gaussian noise and a
combination of both the noise in the same image. It was observed that the
proposed method performed effectively on noise levels up-to 0.5 and was able to
produce results with PSNR values ranging from 20 to 30 in most of the cases.
Excellent reduction rates were observed on salt and pepper noise and moderate
reduction rates were observed on Gaussian noise. Experimental results show that
our proposed system has a wide range of applicability in any domain specific
image denoising scenario, such as medical imaging, mammogram etc.