Abstract
Stereo matching under complex circumstances, such as low-textured areas and high dynamic range (HDR) scenes, is an ill-posed problem. In this paper, we introduce a stereo matching approach for real-world HDR scenes which is backward compatible to conventional stereo matchers. For this purpose, (1) we compare and evaluate the tone-mapped disparity maps to find the most suitable tone-mapping approach for the stereo matching purpose. Thereof, (2) we introduce a combining graph-cut based framework for effectively fusing the tone-mapped disparity maps obtained from different tone-mapped input image pairs. And finally, (3) we generate reference ground truth disparity maps for our evaluation using the original HDR images and a customized stereo matching method for HDR inputs. Our experiments show that, combining the most effective features of tone-mapped disparity maps, an improved version of the disparity is achieved. Not only our results reduce the low dynamic range (LDR), conventional disparity errors by the factor of 3, but also outperform the other well-known tone-mapped disparities by providing the closest results to the original HDR disparity maps.
Highlights
High dynamic range (HDR) images provide greater detail and larger brightness levels than conventional low dynamic range (LDR) ones
There is a trend towards HDR imaging, which fuses several images, acquired with different exposures, into a single, HDR radiance map whose pixel values are proportional to true scene radiance [42]
6 Conclusions We proposed a novel framework for combining several tone-mapped disparity maps in order to reduce the number of incorrect matching points and improve the performance of image matching in the HDR scenes
Summary
High dynamic range (HDR) images provide greater detail and larger brightness levels than conventional low dynamic range (LDR) ones. Working in HDR space can lead to better results by using more detailed brightness information. The pixel values in HDR space are calculated using an estimated camera response function to fuse the multiple photographs into a single, high dynamic range radiance map whose pixel values are proportional to the true radiance values in the scene. Exposed images are used to estimate the camera response function [1]. It is not hard to predict that working in HDR space provides more informative disparities. This is especially true in challenging lighting conditions
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