A sparse linear model for saliency-guided decolorization

Different from most existing decolorization techniques that emphasize preserving image features revealed in the input color space, our proposed method focuses on exploring those in a higher-dimensional feature space. The shift of paradigm is motivated by that decolorization is often sensitive to adopting the various color systems. The results of converting the same color image expressed in different color spaces could vary significantly. We instead consider constructing an image-dependent feature space by learning a representative dictionary, and carry out decolorizing an image by retaining the structures there. To this end, for a given image, the atoms of the dictionary are systematically collected to reflect the visually important/salient contents, and also to concisely reduce chromatic redundancy. A sparse linear model with respect to the learned dictionary is then assumed. Finally, a linear projection to grayscale respecting the inner products in the feature space can be optimized to accomplish the conversion.