A Parametric Method for Remapping and Calibrating Fisheye Images for Glare Analysis

High Dynamic Range (HDR) imaging using a fisheye lens has provided new opportunities to evaluate the luminous environment in visual comfort research. For glare analysis, strict calibration is necessary to extract accurate luminous maps to achieve reliable glare results. Most studies have focused on correcting the vignetting effect in HDR imaging during post-calibration. However, the lens projection also contributes to luminous map errors because of its inherent distortion. To date, there is no simple method to correct this distortion phenomenon for glare analysis. This paper presents a parametric-based methodology to correct the projection distortion from fisheye lenses for the specific use in glare analysis. HDR images were captured to examine two devices: a 190&deg
equisolid SIGMA 8 mm F3.5 EX DG fisheye lens mounted on a Canon 5D camera, and a 195&deg
fisheye commercial lens with an unknown projection, mounted on the rear camera of a Samsung Galaxy S7. A mathematical and geometrical model was developed to remap each pixel to correct the projection distortion using Grasshopper and MATLAB. The parametric-based method was validated using Radiance and MATLAB through checking the accuracy of pixel remapping and measuring color distortion with Structural Similarity Index (SSIM). Glare scores were used to compare the results between both devices, which validates the use of mobile phones in photometric research. The results showed that this method can be used to correct HDR images projection distortion for more accurate evaluation of the luminous environment in glare research.