denoisemethodes

Denoisemethodes, or denoising methods, refer to techniques used to remove noise from signals, images, and video while preserving important structures and details. They are applied across various domains, including photography, medical imaging, astronomy, and remote sensing, and aim to improve clarity, accuracy, and interpretability of data.

Methods are often categorized by domain and principle. In the spatial domain, linear filters such as Gaussian or Wiener filters reduce noise by averaging neighboring samples, while nonlinear filters like median filters excel at removing impulse noise and preserving edges. Adaptive and edge-preserving filters, such as bilateral filtering, aim to smooth noise while preserving discontinuities. In the transform domain, noise is attenuated after transforming the data into a different representation, using techniques like DCT, wavelet shrinkage, or more advanced methods such as BM3D, which groups similar blocks to perform collaborative filtering.

Sparse representation and dictionary learning approach denoising by expressing the signal as a sparse combination of basis elements, followed by thresholding or optimization. Non-local and patch-based methods, including non-local means and BM3D variants, exploit redundancy by comparing patches across the image. Recently, deep learning methods have become dominant, training neural networks to map noisy inputs to clean outputs. These include supervised denoisers (for known noise models), blind or self-supervised approaches that do not require paired clean data, and generative or adversarial models aimed at improving perceptual quality.

Common noise models include additive white Gaussian noise, Poisson noise in photon-limited imaging, and impulse or salt-and-pepper noise. Evaluation typically uses PSNR and SSIM, though perceptual quality is increasingly emphasized. The field continues to balance noise suppression, detail preservation, and computational efficiency.