Entropic Regularisation of Robust Optimal Transport

Grogan et al. [11, 12] have recently proposed a solution to colour transfer by minimising the Euclidean distance L2 between two probability density functions capturing the colour distributions of two images (palette and target). It was shown to be very competitive to alternative solutions based on Optimal Transport for colour transfer. We show that in fact Grogan et al’s formulation can also be understood as a new robust Optimal Transport based framework with entropy regularisation over marginals

Directive local color transfer based on dynamic look-up table

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Entropic Regularisation of Robust Optimal Transport

Grogan et al. [11, 12] have recently proposed a solution to colour transfer by minimising the Euclidean distance L2 between two probability density functions capturing the colour distributions of two images (palette and target). It was shown to be very competitive to alternative solutions based on Optimal Transport for colour transfer. We show that in fact Grogan et al’s formulation can also be understood as a new robust Optimal Transport based framework with entropy regularisation over marginals

Saliency Detection Gradient Preservation for Bayer Image Color Reconstruction

Image color reconstruction is a necessary process to recover high quality full color images from Bayer images. In view of the existence of image texture and edge blurring in color reconstruction algorithms, a four-direction joint gradient weighted residual interpolation algorithm is proposed, which uses four-direction weights obtained from RGB pixel gradients and residual gradients in Bayer images, linearly combined with the color difference estimation to effectively obtain the full G image. Aiming at the color cast phenomenon of the image after color interpolation, a saliency detection gradient-preserving color correction algorithm is proposed based on the RGB image captured under natural light. Firstly, the saliency detection method is used to segment the interpolated image and the RGB image into two regions, then carrying out the region correspondence for gradient-preserving color correction, and finally the weighted fusion method is used to obtain the final color reconstructed image. The experimental results show that the reconstructed image texture and edges are clearer and the colors are closer to RGB images

L2 Divergence for robust colour transfer

Optimal Transport (OT) is a very popular framework for performing colour transfer in images and videos. We have proposed an alternative framework where the cost function used for inferring a parametric transfer function is defined as the robust 2 divergence between two probability density functions (Grogan and Dahyot, 2015). In this paper, we show that our approach combines many advantages of state of the art techniques and outperforms many recent algorithms as measured quantitatively with standard quality metrics, and qualitatively using perceptual studies (Grogan and Dahyot, 2017). Mathematically, our formulation is presented in contrast to the OT cost function that shares similarities with our cost function. Our formulation, however, is more flexible as it allows colour correspondences that may be available to be taken into account and performs well despite potential occurrences of correspondence outlier pairs. Our algorithm is shown to be fast, robust and it easily allows for user interaction providing freedom for artists to fine tune the recoloured images and videos (Grogan et al., 2017)