Two self-supervised image denoiser designs with discrete wavelet transform and non-local means-based algorithms

Image denoising is crucial in applications like medical imaging and photography, where restoring high-quality images from noisy data is essential. Traditional techniques often struggle with complex noise patterns, while deep learning-based methods typically rely on clean-noisy image pairs for training, limiting their practicality. Additionally, deep learning approaches face challenges such as the lack of ground truth clean images, sensitivity to specific noise types, and the introduction of artifacts during processing. In this work, we propose two novel self-supervised denoising approaches: a Discrete Wavelet Transform (DWT)-based model and a Non-Local Means (NLM)-based model. The DWT-based approach employs wavelet decomposition to separate image details across multiple frequency scales, selectively suppressing high-frequency noise via soft thresholding while preserving low-frequency components. The resulting wavelet coefficients are used to create pseudo-clean targets for training a U-Net architecture, ensuring effective denoising while maintaining structural integrity. The NLM-based approach leverages redundancy in image patches by applying the NLM algorithm to generate pseudo-clean targets through patch similarity-based averaging. These targets train a U-Net model with a custom loss function that balances Mean Squared Error (MSE), Peak Signal-to-Noise Ratio (PSNR), and Structural Similarity Index (SSIM), optimizing perceptual quality. Both models are trained on 5,000 noisy images from the ImageNet validation set without relying on clean references. Validated on synthetic Gaussian and Poisson noise at varying magnitudes, the DWT-based model achieved Mean PSNR and SSIM values of 31.07 and 0.9279, respectively, while the NLM-based model attained 30.17 and 0.9303. These results demonstrate the robustness and effectiveness of the proposed methods, making them suitable for real-world applications such as medical diagnostics and low-light photography.