An Extension Application of 1D Wavelet Denoising Method for Image Denoising

Signals are of a one-dimensional mathematical model or 1D model although n-dimensional signals exist. A wavelet is a wave-oscillation structure with an amplitude. The amplitudes may begin from zero and either increase or decrease and also return to zero many times, which forms an iteration process. Denoising is the process of removing noise or blur from signals and images for better human visualization. In the proposed algorithm, we apply the mean algebraic method to denoise the two-dimensional (2D) signals or images obtained from previous stages or steps before adding them to the new iterations. The signal is a 1D mathematical function whereas the image is a two-dimensional or 2D mathematical function. So, in the proposed method, we extended the applications of the 1D wavelet denoising method for 2D mathematical functions or images for denoising. The final result of the extension application of the 1D wavelet denoising method produced better visual quality images compared to the existing method (i.e., 1D wavelet toolbox denoising method) of noisy images. With the extension application of the 1D wavelet denoising method, the denoising percentage improved by about 78% compared to the existing method. Since the denoising percentage of the proposed method is so good it can achieve 100% accuracy of the original image in further research.