PERFORMANCE IMPROVEMENT OF CURVELET TRANSFORM BASED IMAGE DENOISING USING GRADIENT DESCENT

Computer Engineering

Image denoising is a very important preprocessing in many computer vision tasks. Removal of noise is an important step in the image restoration process, but denoising of image remains a challenging problem in recent research associate with image processing. Denoising is used to remove the noise from corrupted image, while retaining the edges and other detailed features as much as possible. This noise gets introduced during acquisition, transmission & reception and storage & retrieval processes. The major objective of this is how to remove noise present in image using non-linear technique such as curvelet transform and wavelet transform in image processing. In this I used Gradient Descent Algorithm gradient descent is useful for large signal. It works efficiently on large signal.We can apply the gradient descent algorithm to the Wiener filter in such a way that at each new step we can calculate a new set of filter coefficients. and these new set of filter coefficient makes filtering more efficient. Using the gradient descent method, we can approach a minimum error value in relatively few iterations.

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