Denoisen

Denoisen is the process of removing noise from data, typically from signals, images, or audio, to recover the underlying true signal. It aims to reduce random fluctuations introduced by acquisition or transmission while preserving salient structures. Denoisen can be applied to one-dimensional signals (audio, electrocardiograms) or two- or three-dimensional data (images, videos), and may target different noise models such as Gaussian, salt-and-pepper, or speckle.

In image and video processing, denoisen seeks to remove grain without blurring edges. In audio, it reduces hiss or background noise while preserving speech or music. In biomedical engineering, denoisen can improve the reliability of diagnostic signals. In remote sensing and astronomy, denoisen helps reveal faint features and improve the detectability of small signals.

Methods are broadly categorized as classical and modern. Classical methods include spatial filters (mean, median), Gaussian smoothing, and Wiener filters. Transform-domain techniques include wavelet thresholding and discrete cosine transform thresholding. More advanced methods include non-local means, BM3D, and sparse representation. Deep learning approaches use denoising autoencoders and convolutional networks trained to map noisy inputs to clean outputs, sometimes leveraging large datasets.

Evaluation relies on objective metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), as well as visual or task-based assessments. The choice of method depends on the noise model, data type, and tolerance for artifacts or loss of details. Denoisen often involves trade-offs between noise suppression and detail preservation.