Sliced $\mathcal{L}_2$ Distance for Colour Grading
This addresses color grading challenges in image processing, though it appears incremental as it builds on existing distribution mapping approaches.
The paper tackles the problem of color transfer between images with overlapping scenes by proposing a new method that maps one N-dimensional distribution to another using sliced L2 distance with iterative projection. The results show quantitatively and qualitatively competitive performance compared to state-of-the-art color transfer methods.
We propose a new method with $\mathcal{L}_2$ distance that maps one $N$-dimensional distribution to another, taking into account available information about correspondences. We solve the high-dimensional problem in 1D space using an iterative projection approach. To show the potentials of this mapping, we apply it to colour transfer between two images that exhibit overlapped scenes. Experiments show quantitative and qualitative competitive results as compared with the state of the art colour transfer methods.