Digital cameras are often used in recent days for photographic documentation in medical sciences. However, color reproducibility of same objects suffers from different illuminations and lighting conditions. This variation in color representation is problematic when the images are used for segmentation and measurements based on color thresholds. In this paper, motivated by photographic follow-up of chronic wounds, we assess the impact of (i) gamma correction, (ii) white balancing, (iii) background unification, and (iv) reference card-based color correction. Automatic gamma correction and white balancing are applied to support the calibration procedure, where gamma correction is a nonlinear color transform. For unevenly illuminated images, non- uniform illumination correction is applied. In the last step, we apply colorimetric calibration using a reference color card of 24 patches with known colors. A lattice detection algorithm is used for locating the card. The least squares algorithm is applied for affine color calibration in the RGB model. We have tested the algorithm on images with seven different types of illumination: with and without flash using three different off-the-shelf cameras including smartphones. We analyzed the spread of resulting color value of selected color patch before and after applying the calibration. Additionally, we checked the individual contribution of different steps of the whole calibration process. Using all steps, we were able to achieve a maximum of 81% reduction in standard deviation of color patch values in resulting images comparing to the original images. That supports manual as well as automatic quantitative wound assessments with off-the-shelf devices.