Correcting over-exposure in photographs

This paper introduces a method to correct over-exposure in an existing photograph by recovering the color and lightness separately. First, the dynamic range of well exposed region is slightly compressed to make room for the recovered lightness of the over-exposed region. Then the lightness is recovered based on an over-exposure likelihood. The color of each pixel is corrected via neighborhood propagation and also based on the confidence of the original color. Previous methods make use of ratios between different color channels to recover the over-exposed ones, and thus can not handle regions where all three channels are over-exposed. In contrast, our method does not have this limitation. Our method is fully automatic and requires only one single input photo. We also provide users with the flexibility to control the amount of over-exposure correction. Experiment results demonstrate the effectiveness of the proposed method in correcting over-exposure.

[1]  Chiou-Shann Fuh,et al.  Tone Reproduction: A Perspective from Luminance-Driven Perceptual Grouping , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[2]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH '08.

[3]  Wolfgang Heidrich,et al.  Ldr2Hdr: on-the-fly reverse tone mapping of legacy video and photographs , 2007, SIGGRAPH 2007.

[4]  Erik Reinhard,et al.  Photographic tone reproduction for digital images , 2002, ACM Trans. Graph..

[5]  Wolfgang Heidrich,et al.  Ldr2Hdr: on-the-fly reverse tone mapping of legacy video and photographs , 2007, ACM Trans. Graph..

[6]  Kun Zhou,et al.  High dynamic range image hallucination , 2007, SIGGRAPH '07.

[7]  Xuemei Zhang,et al.  Estimation of saturated pixel values in digital color imaging. , 2004, Journal of the Optical Society of America. A, Optics, image science, and vision.

[8]  Roberto Manduchi,et al.  Bilateral filtering for gray and color images , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[9]  Dani Lischinski,et al.  Colorization using optimization , 2004, ACM Trans. Graph..

[10]  Dani Lischinski,et al.  Gradient Domain High Dynamic Range Compression , 2023 .

[11]  Xiaopeng Zhang,et al.  Enhancing photographs with Near Infra-Red images , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Jiejie Zhu,et al.  Automatic Correction of Saturated Regions in Photographs using Cross‐Channel Correlation , 2009, Comput. Graph. Forum.

[13]  Shree K. Nayar,et al.  Radiometric self calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).