We propose a framework to perform auto-radiometric calibration in photometric stereo methods to estimate surface orientations of an object from a sequence of images taken using a radiometrically uncalibrated camera under varying illumination conditions. Our proposed framework allows the simultaneous estimation of surface normals and radiometric responses, and as a result can avoid cumbersome and time-consuming radiometric calibration. The key idea of our framework is to use the consistency between the irradiance values converted from pixel values by using the inverse response function and those computed from the surface normals. Consequently, a linear optimization problem is formulated to estimate the surface normals and the response function simultaneously. Finally, experiments on both synthetic and real images demonstrate that our framework enables photometric stereo methods to accurately estimate surface normals even when the images are captured using cameras with unknown and nonlinear response functions.

In this paper we propose a novel method to inpaint highlights and to remove the specularity in the image with specular objects by the color line projection. Color line projection is the method that a color with a surface reflection component is projected near the diffuse color line by following the direction of the specular color line. We use two captured images using different exposure time so that the clue of the original color in a highlight area is searched from two images since the color at the highlight region is distorted and saturated to the illumination color. In the first step of the proposed procedure, the region corresponding to the highlight is generated and the clue of the original highlight color is acquired. In the next step, the color line is generated by the restricted region growing method around the highlight region, and the color line is divided into the diffuse color line and the specular color line. In the final step, pixels near the specular color line are projected onto near the diffuse color line by the color line projection, in which the modified random function is applied to realistically inpaint the highlight. One of advantages in our method is to find the highlight region and the clue of the original color of the highlight with ease. It also efficiently estimates the surface reflection component which is utilized to remove specularity and to inpaint the highlight. The proposed method performs the highlight inpainting and the specular removal simultaneously once the color line is generated. In addition, color line projection with the modified random function can make the result more realistic. We show experimental results from the real images and make a synthesis of the real image and the image modified by the proposed method.