Author(s)

M. Sasikala Devi¹, R. Sukumar²

¹Department of Computer Science and Engineering, University College of Engineering, Anna University, Dindigul, India.
²Department of Computer Science and Engineering, Sethu Institute of Technology, Kariyapatti, Virudhunagar, India.

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.

Adaptive Block Selection, Enhancement Filtering, Image Denoising, Noise Identification, Particle Swarm Optimization

Devi, M. and Sukumar, R. (2016) Metaheuristic Based Noise Identification and Image Denoising Using Adaptive Block Selection Based Filtering. Circuits and Systems, 7, 2729-2751. doi: 10.4236/cs.2016.79235.