Diffraction effects detection for HDR image-based measurements

Abstract

Modern imaging techniques have proved to be very efficient to recover a scene with high dynamic range (HDR) values. However, this high dynamic range can introduce star-burst patterns around highlights arising from the diffraction of the camera aperture. The spatial extent of this effect can be very wide and alters pixels values, which, in a measurement context, are not reliable anymore. To address this problem, we introduce a novel algorithm that, utilizing a closed-form PSF, predicts where the diffraction will affect the pixels of an HDR image, making it possible to discard them from the measurement. Our approach gives better results than common deconvolution techniques and the uncertainty values (convolution kernel and noise) of the algorithm output are recovered. References and links1. E. Reinhard, High dynamic range imaging : acquisition, display, and image-based lighting (Morgan Kaufmann/Elsevier, 2010). 2. A. N. A. N. Tikhonov and V.

Extrapolation of Saturated Diffraction Spikes in Photographs Containing Light Sources

“…For straight edge regular polygons, these shapes are pointy start with arms either the same (even) or double (odd) the number of the sides [3,5,13]. However, the model becomes significantly more complicated if the edges of the polygons, as in the case of usual camera apertures, are slightly curved [13][14][15][16]. Our cameras used in the experiments had slightly curved hexagonal apertures, but we used the straight edge model for calculations, as it is available as a closed-form equation [3,6].…”

“…The images were taken using a Basler ace acA800-510um monochromatic camera with a resolution of 832 × 632 pixels and 10-bit depth. Each scene was recorded with multiple exposures and assembled into HDR images [15,17], where every single pixel contains valid information (not saturated or below noise level). The exposure times increase by a factor of 2 with each new take, and for each pixel, the highest non-saturated value is selected, then weighed back to the shortest exposure time.…”

Quad‐Based Fourier Transform for Efficient Diffraction Synthesis

“…Several parametric functions admit closed‐form solutions, including sunburst patterns [YF74], polygonal shapes [LM83, MS91], and triangular meshes [HCL91,LLNZ11]. The majority of such closed‐form solutions are applied for electromagnetic analyses or the detection of diffraction [LHP17]. Rendering of dispersion and near‐field ringing has hardly been explored with this direction.…”

Computational Imaging Prediction of Starburst-Effect Diffraction Spikes

“…Diffraction spikes in telescope images of stars and other illuminated bodies 1 – 3 introduce uncertainties in luminosity-dependent measurements, but can be useful in localization techniques 4 . The phenomenon occurs on all light sources and affects a wide range of imaging systems, including photography 5 – 7 , medical endoscopy 8 , and telemetry acquisition systems 9 , with higher-intensity sources yielding more prominent spikes.…”

“…The suppression and intensification of the starburst effect have received much attention to date. Efforts have been made to render visually similar effects in image post-processing 6 and minimize diffraction artifacts in high dynamic range (HDR) photography 5 . In astronomy, software modelling Bahtinov masks and spider-diffraction have been developed 16 , and the reduction of diffractive effects on segmented mirrors is crucial for telescope design 17 .…”