Localised decorrelation stretch (L-DCS) for improved visibility of large, dense rock art scenes

DStretch® is widely known within the study of rock art and is used across the field of archaeology to enhance the visibility of faded or difficult to see motifs. DStretch calculates enhancements based on all RGB values in an image and so often struggles to decorrelate local areas with high correlation, particularly if there is a wide distribution of RGB values such as in dense rock art scenes, orthomosaics, or 3D model textures. Local decorrelation stretch (L-DCS) automatically separates an image into overlapping windows, performing decorrelated stretching on each, and merging them back into a single image. The results retained greater fine details in motifs and improved significantly the visibility of “hidden” motifs in densely painted areas when compared to DStretch. The flexibility afforded by our approach and the consistency of results in different scenarios make L-DCS a robust approach for automated DCS on larger datasets without manual intervention. • A more robust method for enhancing rock art motifs in dense scenes. • L-DCS can handle large images more effectively. • L-DCS handles greater variation in pigment colours without the need for multiple filters.

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Publication Details

Journal
Digital Applications in Archaeology and Cultural Heritage
Published
2026-02-23
DOI
https://doi.org/10.1016/j.daach.2026.e00522
Primary Topic
Image Processing and 3D Reconstruction
Type
article
Field-Weighted Citation Impact
0.00
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article

Localised decorrelation stretch (L-DCS) for improved visibility of large, dense rock art scenes

Ines Tacon, Andrea Jalandoni, Benedict Dyson
Digital Applications in Archaeology and Cultural Heritage
Image Processing and 3D Reconstruction
article

Localised decorrelation stretch (L-DCS) for improved visibility of large, dense rock art scenes

Ines Tacon, Andrea Jalandoni, Benedict Dyson
article en

Abstract

DStretch® is widely known within the study of rock art and is used across the field of archaeology to enhance the visibility of faded or difficult to see motifs. DStretch calculates enhancements based on all RGB values in an image and so often struggles to decorrelate local areas with high correlation, particularly if there is a wide distribution of RGB values such as in dense rock art scenes, orthomosaics, or 3D model textures. Local decorrelation stretch (L-DCS) automatically separates an image into overlapping windows, performing decorrelated stretching on each, and merging them back into a single image. The results retained greater fine details in motifs and improved significantly the visibility of “hidden” motifs in densely painted areas when compared to DStretch. The flexibility afforded by our approach and the consistency of results in different scenarios make L-DCS a robust approach for automated DCS on larger datasets without manual intervention. • A more robust method for enhancing rock art motifs in dense scenes. • L-DCS can handle large images more effectively. • L-DCS handles greater variation in pigment colours without the need for multiple filters.

Digital Applications in Archaeology and Cultural HeritageVol. 40
Griffith University (AU)
Sustainable cities and communities
Openalex Percentile: Top 49%
Image Processing and 3D Reconstruction
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