International Journal of Soft Computing And Artificial Intelligence (IJSCAI)
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  Journal Paper

Paper Title
Image Denoising using Adaptive Scaling

Images captured with both digital cameras and conventional film cameras will gather in some noise from a variety of sources. Many further uses of these images require that the noise must be removed. So image denoising is vital both as a process itself and as a pre-processing stage in other operations. It basically means the process of removing noise from a signal. Image denoising has been a well studied problem in the field of image processing. Yet researchers continue to focus attention on it to better the current state-of-the-art. It involves manipulation of the image data to produce a visually high quality image. The main property of a good image denoising model is that it will remove noise while preserving edges. Keeping this in mind, we have presented a robust method for image denoising. The input image characterised by Gaussian noise is pre-processed where in the color image is converted to gray scale. 1st level haar wavelet decomposition is performed on this image to convert it from time domain to frequency domain signal. Further in adaptive scaling stage the frequency signal is manipulated according to the trained co-efficients. The trained co-efficients are generated according to the procedure explained by Roth for modelling image priors. Once this process is complete, inverse wavelet transform is applied to convert the signal back to time domain and the resulting image would be a noise free image.

Author - Shylaja.S.L, Vinayadatta V Kohir .

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| Published on 2014-11-08
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