A Joint Salt and Pepper Noise Removal and Resolution Enhancement Algorithm Using Wavelet Transform and Neural Networks
Shirin Salehi, Homayoun Mahdavi-Nasab, Hossein Pourghassem
International Journal of Information Studies , Vol. 3, No. 4, 2011.
DOI: 
Link: http://www.istudies.net/journal/sites/default/files/A%20Joint%20Salt%20and%20Pepper%20Noise%20Removal%20and%20Resolution%20Enhancement%20Algorithm%20Using%20Wavelet%20Transform%20and%20Neural%20Networks.pdf
Abstract:
One strategy for interpolation of noisy images is to denoise the image first and then interpolate the denoised image. However, this strategy may not lead to satisfying results because denoising may smooth image details and introduce some artifacts. These artifacts will also be amplified in the following interpolation stage. In this paper we propose a joint salt and pepper noise removal and resolution enhancement algorithm using wavelet transform and multilayer perceptron (MLP) neural networks. We implement both discrete wavelet transform and dual-tree complex wavelet transform. The wavelet subbands corresponding to the noise free high-resolution image are estimated from the noisy low-resolution image using MLP subband estimators. Therefore, the noise free high-resolution image is obtained by wavelet reconstruction. Taking advantages of complex wavelet transform such as nearly shift invariance, substantially reduced aliasing and directional selectivity the subband estimation is done with higher accuracy. As it is verified in the experimental results, the proposed algorithm has superiority over the other methods both subjectively and objectively and is able to maintain the image fine structures well.