Image aberrations can cause severe degradation in image quality for consumer-level cameras, especially under the current tendency to reduce the complexity of lens designs in order to shrink the overall size of modules. In simplified optical designs, chromatic aberration can be one of the most significant causes for degraded image quality, and it can be quite difficult to remove in post-processing, since it results in strong blurs in at least some of the color channels. In this work, we revisit the pixel-wise similarity between different color channels of the image and accordingly propose a novel algorithm for correcting chromatic aberration based on this cross-channel correlation. In contrast to recent weak prior-based models, ours uses strong pixel-wise fitting and transfer, which lead to significant quality improvements for large chromatic aberrations. Experimental results on both synthetic and real world images captured by different optical systems demonstrate that the chromatic aberration can be significantly reduced using our approach.
|Original language||English (US)|
|Title of host publication||2017 IEEE International Conference on Computer Vision (ICCV)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||9|
|State||Published - Dec 25 2017|
Bibliographical noteKAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported by KAUST baseline funding, as well as a UBC 4YF Doctoral Fellowship. The authors thank Tao Yue, Qiang Fu, and Felix Heide for the help on synthetic results.