driftcorrection

Drift correction refers to a family of techniques used to compensate for slow, gradual changes in measurements or images that occur over time. These drifts can reduce accuracy, blur details, or misalign data if left unaddressed. Drift may arise from thermal expansion and contraction, mechanical flexure, electronic baseline shifts, atmospheric or environmental variations, or sample movement.

In imaging and microscopy, drift causes frames to slide relative to one another during acquisition. Correction approaches combine hardware and software solutions. Hardware methods include active stabilization, autofocus, and closed-loop stage control to minimize motion. Software approaches estimate drift by tracking fiducial markers or image content, using cross-correlation, phase correlation, or optical flow, and then realigning frames with subpixel precision. In some workflows, drift estimates are applied iteratively to improve registration and, if needed, resample or deconvolve images.

Across astronomy and remote sensing, drift is addressed by guiding systems that keep the object centered during long exposures and by post-processing alignment of images from multiple frames. Techniques often overlap with those used in microscopy, including image registration and stacking with quality-weighted alignment.

In signal processing and spectroscopy, drift correction focuses on removing low-frequency baseline changes. Common methods include polynomial detrending, high-pass filtering, Savitzky-Golay smoothing, and Kalman filtering for sequential data. When reference signals are available, they can be used to separate drift from the true signal.

Challenges include distinguishing true signal changes from drift, avoiding overcorrection that distorts features, and handling nonuniform drift. Validation often relies on known references, simulated data, or independent measurements.