thresholded

Thresholded refers to data, images, signals, or models that have undergone thresholding, a process that imposes a boundary value (the threshold) to convert or categorize continuous measurements into discrete levels. A thresholded result typically emphasizes values above (or below) the threshold and suppresses others, producing binary or simplified representations. Thresholding is widely used to reduce noise, segment data, or support decision making based on a single cutoff.

In image processing, thresholding converts grayscale images into binary images. Global thresholding uses a single threshold for all pixels, while adaptive or local thresholding uses varying thresholds based on neighborhood statistics. This helps separate foreground from background or highlight specific features.

In signal processing, thresholding suppresses weak components and preserves stronger signals by setting values below the threshold to zero or by scaling values toward the threshold. This can improve readability or reduce clutter in the signal.

In statistics and machine learning, thresholding appears in feature selection and in denoising techniques such as wavelet thresholding. Hard thresholding sets coefficients below the threshold to zero, while soft thresholding also shrinks coefficients toward zero. Thresholding is also used to convert continuous model scores into discrete class labels by applying a cutoff.

In neuroscience, neurons are often described as thresholded, firing only when input exceeds a certain threshold. The term can also describe data or outputs that have been truncated or clipped to satisfy constraints or privacy requirements.