2 research outputs found
Fast and High Quality Highlight Removal from A Single Image
Specular reflection exists widely in photography and causes the recorded
color deviating from its true value, so fast and high quality highlight removal
from a single nature image is of great importance. In spite of the progress in
the past decades in highlight removal, achieving wide applicability to the
large diversity of nature scenes is quite challenging. To handle this problem,
we propose an analytic solution to highlight removal based on an L2
chromaticity definition and corresponding dichromatic model. Specifically, this
paper derives a normalized dichromatic model for the pixels with identical
diffuse color: a unit circle equation of projection coefficients in two
subspaces that are orthogonal to and parallel with the illumination,
respectively. In the former illumination orthogonal subspace, which is
specular-free, we can conduct robust clustering with an explicit criterion to
determine the cluster number adaptively. In the latter illumination parallel
subspace, a property called pure diffuse pixels distribution rule (PDDR) helps
map each specular-influenced pixel to its diffuse component. In terms of
efficiency, the proposed approach involves few complex calculation, and thus
can remove highlight from high resolution images fast. Experiments show that
this method is of superior performance in various challenging cases.Comment: 11 pages, 10 figures, submitted to IEEE TI
A Uniform Framework for Estimating Illumination Chromaticity, Correspondence, and Specular Reflection
Abstract—Based upon a new correspondence matching invariant called illumination chromaticity constancy, we present a new solution for illumination chromaticity estimation, correspondence searching, and specularity removal. Using as few as two images, the core of our method is the computation of a vote distribution for a number of illumination chromaticity hypotheses via correspondence matching. The hypothesis with the highest vote is accepted as correct. The estimated illumination chromaticity is then used together with the new matching invariant to match highlights, which inherently provides solutions for correspondence searching and specularity removal. Our method differs from the previous approaches: those treat these vision problems separately and generally require that specular highlights be detected in a preprocessing step. Also, our method uses more images than previous illumination chromaticity estimation methods, which increases its robustness because more inputs/constraints are used. Experimental results on both synthetic and real images demonstrate the effectiveness of the proposed method. Index Terms—Chromaticity, dichromatic reflection model, reflection components separation, specular reflection, stereo matching. I