A Joint Salt and Pepper Noise Removal and Resolution Enhancement Algorithm in Complex Wavelet Domain

Subject Areas : Renewable energy

Shirin  Salehi ¹ (MSc/Najafabad Branch, Islamic Azad University)
Homayoun  Mahdavi-Nasab ² (Assistant Professor/Najafabad Branch, Islamic Azad University)
Hossein  Pourghassem ³ (Assistant Professor/Najafabad Branch, Islamic Azad University)

Received: 2010-04-23 Accepted : 2012-02-22 Published : 2012-06-01

Keywords: Salt and pepper noise, image interpolation, complex wavelet transform,

Abstract :

Most of the existing image resolution enhancement algorithms assume that the image is clean and noise free, but this assumption is not practically valid. One strategy for interpolation of noisy images is to denoise the image first and then interpolate the denoised image. However, this strategy does not lead to satisfying results because denoising may smooth image details and also other artifacts such as blurring and blocking introduced due to image denoising will be amplified in the following interpolation stage. Thus, in this paper we propose a joint salt and pepper noise removal and resolution enhancement algorithm using dual-tree complex wavelet transform and feedforward neural networks. In this algorithm, the wavelet subbands corresponding to noise free high resolution image are estimated from noisy low resolution image by multi-layer perceptron (MLP). Therefore the noise free high resolution image is obtained by complex wavelet reconstruction of the estimated subbands. Takeing advantages of complex wavelet transform such as nearly shift invariance and directional selectivity the subband estimation by neural networks is done with high accuracy. As it is verified in the experimental results, the proposed algorithm has better performance both subjectively and objectively and is able to maintain the image fine structures well.