Abstract
Image quality enhancement is an important image processing task, wherein denoising an image is essential for accurate image diagnoses, because the presence of noise in an image produces incorrect information. This paper proposes double density wavelet transform based intelligent techniques for image denoising. This hybrid technique combines wavelet and neural networking methods, performed and validated using different standard images and extant denoising methods. These images were degraded using a variety of noise levels to simulate actual noise degradation. The results reveal the high-peak signal-to-noise ratio of the proposed system, which is thus considered effective compared to state-of-the-art techniques.
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