<![CDATA[Today, I will explain an image denoising method based on 3D block matching with harmonic filtering in the transform domain. This topic is important because digital images are susceptible to noise during acquisition, storage, and transmission. Image denoising is crucial in pre-processing and is a key research area in digital image processing and computer vision. Traditional denoising techniques face limitations such as high computational complexity, so combining multiple methods is more effective. The integration of wave-domain harmonic filtering and 3D block matching (BM3D) introduces a new and efficient denoising algorithm. The Euclidean distance approach is used to group similar 2D image blocks into a 3D array. The inverse transformation reconstructs the image, followed by wavelet decomposition to filter high-frequency noise. To prevent edge blurring, the Laplacian-Gaussian algorithm is applied to refine the diffusion model. Finally, wavelet reconstruction is performed to approximate the original image. Experimental results demonstrate that this approach improves information protection and processing speed, making it highly effective in practice.]]>
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