Multi Resolution And Contrast Enhancement Using Wavelet Transforms and Singular Value Decomposition

In this paper an image resolution and contrast enhancement technique for Additive White Gaussian Noise (AWGN) removal is verified aiming high resolution and contrast image output. In image resolution enhancement, the main loss is on its high frequency components (i.e., edges), which is due to the smoothing caused by interpolation. In order to increase the quality of the super resolved image, preserving the edges is essential. In this work, Discrete Wavelet Transform (DWT) has been employed in order to preserve the high frequency components of the image. Along with DWT, un-decimated wavelets transform, Stationary Wavelet Transform (SWT) for image decomposition and restoration. For signal mixing techniques like bilinear and bi-cubic interpolation were adopted. In the process, the modified noise minimized high frequency sub bands of DWT and SWT are interpolated with the average band of the image. The resultant image then made to undergo inverse DWT for restoration of de-noised image. For Enhancing the contrast of the Image SVD is used with DWT, in which singular value matrix gives the illumination content. By modifying that value the contrast of the given image is increased. The combined use of wavelet transforms and SVD gives the promising applications in finger print reading, mine detection, etc., The proposed method is verified with MATLAB simulations and the experimental results confirms high resolution and contrast of image and been verified with Peak Signal to Noise Ratio (PSNR). The proposed method is superior with PSNR, SSIM when compared with other state of the art methods.