Overdenoising

Overdenoising is a phenomenon in signal and image processing where a denoising operation softens or eliminates not only random noise but also fine structure and details present in the original signal. The result is an overly smooth or blurred output that can look unnatural, especially when high-frequency textures or edges are important. It contrasts with underdenoising, where noise remains visible.

Causes include overly aggressive denoising strength, incorrect estimation of the noise level, or high regularization in optimization-based methods. Thresholding, patch-based averaging, and iterative algorithms can accumulate smoothing when parameters are not tuned to balance noise suppression with detail preservation. Multi-stage pipelines may compound the effect.

Consequences vary by domain but commonly include loss of texture, softened edges, and reduced perceived sharpness. Artifacts such as halos around edges, banding in smooth regions, or unnatural plastic-like skin tones can arise, and in clinical or scientific imaging important features may be obscured.

In practice, overdenoising is evaluated alongside traditional metrics such as PSNR and SSIM, but perceptual quality often matters more. An algorithm can achieve high quantitative scores while producing visually dull results. Users may prefer modest denoising with preserved edges over aggressive smoothing with lower noise but lower perceived fidelity.

Mitigation strategies include adaptive denoising that estimates local noise levels, edge-preserving or texture-preserving methods (for example, bilateral or non-local means variants), and multi-scale approaches. Careful tuning of regularization parameters, stopping criteria for iterative methods, and validation on representative data help reduce the risk of overdenoising.