A REVIEW OF WAVELET TRANSFORM AND KARHUNEN-LOEVE TRANSFORM

VIKRAM SINGH RAJPUT, DEEPAK TOMAR, BHUPESH GOUR

Abstract


ABSTRACT

In the past few decades, many noise reduction techniques have been developed for removing noise and retaining edge details in images. The process of removing noise from the original image is still a demanding problem for researchers. There have been several algorithms and each has its assumptions, merits, and demerits. The prime focus of this dissertation work is related to the processing of an image before it can be used in applications. The processing is done by de-noising of images. In order to achieve this combination of de-noising algorithms are being used. Image denoising algorithms WT (Wavelet transform) and KLT (Karhunen-Loeve transform) are applied on images to remove the noise that are either present in the image during capturing or injected into the image during transmission. The WT shows an excellent performance in the denoise field while KLT shows a good performance in the signal reconstructed ability. De-noising plays a very important role in the field of the image processing. It is often done before the image data is to be analyzed. Denoising is mainly used to remove the noise that is present and retains the significant information, regardless of the frequency contents of the signal. De-noising has to be performed to recover the useful information. The main purpose of an image-denoising algorithm is to eliminate the unwanted noise level while preserving the important features of an image. In this work PSNR, MSE and MAXERR parameters are being improved using combination of  image denoising algorithms WT (Wavelet transform) and KLT (Karhunen-Loeve transform). This paper contains the review of WT and KLT. Wavelet Transform and Karhunen-Loeve Transform (KLT) are used for image enhancement. The WT is known for its denoise ability and KLT for de-correlation ability.


Keywords


Image Denoising, and KLT (Karhunen-Loeve transform), Wavelet transform, Peak Signal to Noise Ratio.Cloud.

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