Instant Edit Propagation on Images Based on Bilateral Grid

The ability to quickly and intuitively edit digital content has become increasingly important in our everyday life.However,existing edit propagation methods for editing digital images are typically based on optimization with high computational cost for large inputs.Moreover,existing edit propagation methods are generally inefficient and highly time-consuming.Accordingly,to improve edit efficiency,this paper proposes a novel edit propagation method using a bilateral grid,which can achieve instant propagation of sparse image edits.Firstly,given an input image with user interactions,we resample each of its pixels into a regularly sampled bilateral grid,which facilitates efficient mapping from an image to the bilateral space.As a result,all pixels with the same feature information(color,coordinates)are clustered to the same grid,which can achieve the goal of reducing both the amount of image data processing and the cost of calculation.We then reformulate the propagation as a function of the interpolation problem in bilateral space,which is solved very efficiently using radial basis functions.Experimental results show that our method improves the efficiency of color editing,making it faster than existing edit approaches,and results in excellent edited images with high quality.

Fast color transfer from multiple images

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 corre- spondences 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.

Edit Propagation via Edge-Aware Filtering

This paper presents a novel framework for efficiently propagating the stroke-based user edits to the regions with similar colors and locations in high resolution images and videos. Our framework is based on the key observation that the edit propagation intrinsically can also be achieved by utilizing recently proposed edge-preserving filters. Therefore, instead of adopting the traditional global optimization which may involve a time-consuming solution, our algorithm propagates edits with the aid of the edge-preserve filters. Such a propagation scheme has low computational complexity and supports multiple kinds of strokes for more flexible user interactions. Further, our method can be easily and efficiently implemented in GPU. The experimental results demonstrate the efficiency and user-friendliness of our approach.

Free Appearance-Editing with Improved Poisson Image Cloning

In this paper, we present a new edit tool for the user to conveniently preserve or freely edit the object appearance during seamless image composition. We observe that though Poisson image editing is effective for seamless image composition. Its color bleeding （the color of the target image is propagated into the source image） is not always desired in applications, and it provides no way to allow the user to edit the appearance of the source image. To make it more flexible and practical, we introduce new energy terms to control the appearance change, and integrate them into the Poisson image editing framework. The new energy function could still be realized using efficient sparse linear solvers, and the user can interactively refine the constraints. With the new tool, the user can enjoy not only seamless image composition, but also the flexibility to preserve or manipulate the appearance of the source image at the same time. This provides more potential for creating new images. Experimental results demonstrate the effectiveness of our new edit tool, with similar time cost to the original Poisson image editing.