Abstract. We propose a sparse and low-rank reflection model for specular highlight detection and removal using a single input image. This
model is motivated by the observation that the specular highlight of a
natural image usually has large intensity but is rather sparsely distributed while the remaining diffuse reflection can be well approximated by a
linear combination of several distinct colors with a sparse and low-rank
weighting matrix. We further impose the non-negativity constraint on
the weighting matrix as well as the highlight component to ensure that
the model is purely additive. With this reflection model, we reformulate
the task of highlight removal as a constrained nuclear norm and l1-norm
minimization problem which can be solved effectively by the augmented
Lagrange multiplier method. Experimental results show that our method
performs well on both synthetic images and many real-world examples
and is competitive with previous methods, especially in some challenging scenarios featuring natural illumination, hue-saturation ambiguity
and strong noises