Data-driven vs. model-driven: Fast face sketch synthesis

Face sketch synthesis refers to the technique generating a sketch from an input photo. Existing methods are data-driven, which synthesize a sketch by linearly combining K candidate sketch patches which are purposely selected from the training data. However, these methods have large computation cost due to neighbor selection process that perform neighbor searching on a large scale of training image patches. Instead of the aforementioned commonly used data-driven strategy, we propose to learn some models from training photos to training sketches which could speed the synthesis process a lot while preserving comparable or even better synthesis performance. Specially, we learn some ridge regressors from training photo patch intensities to training sketch patch intensities. An initial estimation is obtained from these regressors. Simultaneously, a high-frequency image is hallucinated from some ridge regressors which are learned from the high-frequency information of training photos and sketches. The high-frequency image is superimposed to the initial estimation to compensate the filtered details due to the dense average in the initial estimation process. Extensive experiments on public face sketch database illustrate the effectiveness of proposed model-driven strategy.