Symposium on Computational Photography and Video
The research in building HDR images has three main branches: 1) for synthetic images the goal is to simulate the illumination and the light intensity of a virtual scene, commonlly done with renderers like RADIANCE ; 2) for real scenes we can recover HDR radiance maps from photographs captured with a common Low Dynamic Range (LDR) imaging systems, or 3) one can focus the attention on the research of sensors and aquisition hardware with augmented sensitivity to radiance values.
Here we are interested in the recovery of HDR from LDR photographs, that is, to analize a set of images of a real scene taken with different exposures, with the goal of modeling the behaviour of the imaging system. We point out that, if cameras take multiple pictures in a rapid succession, we can use these algorithms as a way to augment sensor sensibility.
The visualization of an HDR image in the low range of a display is a complementary subject of research. It is the problem of mapping scene radiances to display intensities. The challenge is to reduce the dynamic range of an HDR image by a Tone Mappig Operator (TMO) in order to fit in the low dynamic range of the display (that could also be a photographic paper) obtaining a good perceptual result. The film industry has been studying this problem for chemical emulsions since the begining of photography’s history.
Our goal in this work is to employ some recent results obtained by M.D. Grossberg and S.K. Nayar, to recover HDR from photographs using only two differently exposed pictures as input. We take a look at TMO research from the point of view of a photographer that wishes to obtain digital pictures with the same look as if a photographic film were used, to achieve that we simulate film response curves. We are going to integrate the pipeline of recovery and visualization, unifying and simplifying these complementary problems. We are interested in color images, but many techniques of black-and-white photography can be extended for the color context.
Last Update: by lvelho.