Color transfer between images uses the statistics information of image effectively. We present a novel approach of local color transfer between images based on the simple statistics and locally linear embedding. A sketching interface is proposed for quickly and easily specifying the color correspondences between target and source image. The user can specify the correspondences of local region using scribes, which more accurately transfers the target color to the source image while smoothly preserving the boundaries, and exhibits more natural output results. Our algorithm is not restricted to one-to-one image color transfer and can make use of more than one target images to transfer the color in different regions in the source image. Moreover, our algorithm does not require to choose the same color style and image size between source and target images. We propose the sub-sampling to reduce the computational load. Comparing with other approaches, our algorithm is much better in color blending in the input data. Our approach preserves the other color details in the source image. Various experimental results show that our approach specifies the correspondences of local color region in source and target images. And it expresses the intention of users and generates more actual and natural results of visual effect.
To prevent the problem relating to color region mixing, @cite_27 suggested a 3D histogram matching technique to transfer color components in the hue (H), saturation (S), and intensity (I), respectively, in the HSI color space. Albeit, the color information transfer between target and source image can be achieved by this method, the result usually contains notable spatial artifacts and is also dependent on the resolution of input image. @cite_24 @cite_31 resolved the same problem by utilizing an N-dimensional probability density function which matches the 3D color histogram of input images. They used an recursive, nonlinear method that was able to estimate the transformation function by employing a one-dimensional marginal distribution. This technique is potent enough to match the color pallet of target and source images, but it often demands further processing to get rid of noise and spatial visual artifacts.
@cite_24 @cite_27 @cite_31
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This article proposes an original method to estimate a continuous transformation that maps one N-dimensional distribution to another. The method is iterative, non-linear, and is shown to converge. Only 1D marginal distribution is used in the estimation process, hence involving low computation costs. As an illustration this mapping is applied to color transfer between two images of different contents. The paper also serves as a central focal point for collecting together the research activity in this area and relating it to the important problem of automated color grading We present new methods which transfer the color style of a source image into an arbitrary given target image having a different 3D color distribution. The color transfer has a high importance ensuring a wide area of applications from artistic transformation of the color atmosphere of images until different scientific visualizations using special gamut mappings. Our technique use a permissive, or optionally strict, 3D histogram matching, similarly to the sampling of multivariable functions applying a sequential chain of conditional probability density functions. We work by order of hue, hue dependent lightness and from both dependent saturation histograms of source and target images, respectively. We apply different histogram transformations, like smoothing or contrast limitation, in order to avoid some unexpected high gradients and other artifacts. Furthermore, we use dominance suppression optionally, by applying sub-linear functions for the histograms in order to get well balanced color distributions, or an overall appearance reflecting the memory color distribution better. Forward and inverse operations on the corresponding cumulative histograms ensure a continuous mapping of perceptual attributes correlating to limited derivatives. Sampling an appropriate fraction of the pixels and using perceptually accurate and continuous histograms with minimal size as well as other gems make this method robust and fast. This article proposes an original method for grading the colours between different images or shots. The first stage of the method is to find a one-to-one colour mapping that transfers the palette of an example target picture to the original picture. This is performed using an original and parameter free algorithm that is able to transform any N-dimensional probability density function into another one. The proposed algorithm is iterative, non-linear and has a low computational cost. Applying the colour mapping on the original picture allows reproducing the same 'feel' as the target picture, but can also increase the graininess of the original picture, especially if the colour dynamic of the two pictures is very different. The second stage of the method is to reduce this grain artefact through an efficient post-processing algorithm that intends to preserve the gradient field of the original picture.