PostProcessing

Postprocessing refers to operations applied after data has been acquired or generated, with the aim of improving quality, interpretability, or usefulness. It follows the initial capture or computation stage and may include calibration, correction, enhancement, fusion, or summarization. In some workflows the line between postprocessing and preprocessing is blurred; the term generally denotes steps taken to refine results before reporting or further use.

In digital imaging and video, postprocessing covers edits that change appearance or representation: exposure and white balance adjustments, color grading, noise reduction, sharpening, distortion correction, and color space conversion. More advanced tasks include high dynamic range composition, panorama stitching, deconvolution, retouching, compositing, and stabilization. Modern pipelines often favor non-destructive edits and automated processing to produce consistent final outputs.

In scientific, medical, and data-analytic contexts, postprocessing includes steps after data collection or initial modelling to extract signals and reduce artifacts. Examples are sensor calibration, filtering and denoising, image registration, segmentation, visualization, and summary statistics. In machine learning, postprocessing may calibrate probabilities, apply decision thresholds, ensemble results, or adjust outputs for fairness or interpretability.

Quality, safety, and reproducibility considerations are central. Postprocessing decisions should be transparent, with documentation of methods and provenance. When applied to clinical data or regulatory contexts, postprocessing may require validation, auditing, and adherence to standards to avoid introducing bias or misleading conclusions.