Image Denoising Using 1D Wavelet Transform
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In this documentation, we can utilize 1D wavelet transform for image denoising applications. This approach demonstrates significantly lower computational complexity, thereby enhancing processing efficiency and computational speed. The implementation typically involves applying wavelet decomposition to image rows/columns separately, thresholding detail coefficients using methods like soft/hard thresholding, and reconstructing the denoised image through inverse wavelet transform. Additionally, 1D wavelet transform helps better preserve image detail information while effectively removing noise, resulting in superior denoising outcomes. The method can be efficiently implemented using wavelet functions like 'db4' or 'sym8' with proper threshold selection strategies. Therefore, employing 1D wavelet transform for image denoising proves to be a highly effective approach that balances computational efficiency with quality preservation.
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