Abstract

This paper presents a novel guided image filteringmethod using multipoint local polynomial approximation(LPA) with range guidance. In our method, the LPA is ex-tended from a pointwise model into a multipoint model forreliable filtering and better preserving image spatial variation which usually contains the essential information in theinput image. In addition, we develop a scheme with con-stant computational complexity (invariant to the size of fil-tering kernel) for generating a spatial adaptive support re-gion around a point. By using the hybrid of the local poly-nomial model and color/intensity based range guidance, the proposed method not only preserves edges but also does amuch better job in preserving spatial variation than existing popular filtering methods. Our method proves to be effective in a number of applications: depth image upsampling, joint image denoising, details enhancement, and image abstraction. Experimental results show that our method produces better results than state-of-the-art methods and it is also computationally efficient.