Image Transformation using Limited Reference with Application to Photo-Sketch Synthesis

Image transformation refers to transforming images from a source image space to a target image space. Contemporary image transformation methods achieve this by learning coupled dictionaries from a set of paired images. However, in practical use, such paired training images are not easy to get especially when the target image style is not fixed. Thus in most cases, the reference is limited. In this paper, we propose a sparse representation based framework of transforming images with limited reference, which can be used for the typical image transformation application, photo-sketch synthesis. In the learning stage, the edge features are utilized to map patches between different style images, thus building the coupled database for dictionary learning. In the reconstruction stage, sparse representation can well preserve the basic structure of image contents. In addition, a texture synthesis strategy is introduced to enhance target-like textures in the output image. Experimental results show that the performance of our method is comparable to state-of-the-art methods even with limited reference, which is very efficient and less restrictive for practical use.