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colorHarmonization

This module provides tools for color harmonization across a set of images. It adjusts the color/gain of images so that overlapping regions share a consistent appearance.

Overview

Color harmonization is the process of making color-related properties (gain, offset) consistent across a set of images that share common content. This is particularly important in photogrammetry pipelines where many overlapping images are combined.

The approach is based on L∞ optimization of pairwise color histogram differences across an image graph, as described in:

[1] P. Moulon, P. Monasse, R. Marlet. Adaptive Structure from Motion with a Contrario Model Estimation. ACCV 2012.

Common Data Strategies

The module defines a base class CommonDataByPair and three concrete strategies for computing the overlapping regions (masks) between image pairs:

  • CommonDataByPair_fullFrame: uses the entire image frame as the overlap mask
  • CommonDataByPair_matchedPoints: uses matched feature points to define the region of interest
  • CommonDataByPair_vldSegment: uses VLD (Virtual Line Descriptor) segments to define the overlap region
// Example: using matched points to compute color histograms
std::unique_ptr<CommonDataByPair> dataProvider =
    std::make_unique<CommonDataByPair_matchedPoints>(leftImagePath, rightImagePath, matches);

image::Image<unsigned char> maskLeft, maskRight;
dataProvider->computeMask(maskLeft, maskRight);

Gain/Offset Constraint Builder

The GainOffsetConstraintBuilder class builds a linear program that enforces consistent gain and offset parameters across image pairs, minimizing the L∞ norm of histogram alignment errors.

API

  • CommonDataByPair::computeMask(maskLeft, maskRight) — Compute binary masks for the two images.
  • CommonDataByPair::computeHisto(histo, mask, channelIndex, image) — Compute a color histogram for the masked region of an image channel.
  • GainOffsetConstraintBuilder — Build linear constraints for gain/offset optimization.