Recovering the shape and reflectance of non-Lambertian surfaces remains a challenging problem in computer vision since the view-dependent appearance invalidates traditional photo-consistency constraint. In this paper, we introduce a novel concentric multi-spectral light field (CMSLF) design that is able to recover the shape and reflectance of surfaces of various materials in one shot. Our CMSLF system consists of an array of cameras arranged on concentric circles where each ring captures a specific spectrum. Coupled with a multi-spectral ring light, we are able to sample viewpoint and lighting variations in a single shot via spectral multiplexing. We further show that our concentric camera and light source setting results in a unique single-peak pattern in specularity variations across viewpoints. This property enables robust depth estimation for specular points. To estimate depth and multi-spectral reflectance map, we formulate a physics-based reflectance model for the CMSLF under the surface camera (S-Cam) representation. Extensive synthetic and real experiments show that our method outperforms the state-of-the-art shape reconstruction methods, especially for non-Lambertian surfaces.

Registration between low-resolution images is a crucial step in super-resolution. Conventional methods tend to separate scale estimation from translation and rotation estimation. This is because the scale parameter is inherently related to the image resolution. In this paper, we present an area-based image registration technique that can simultaneously estimate translation, rotation, and scale parameters and also take into account differences in resolution between two images. We first develop a scale-space model that relates each reference pixel to a single observation pixel with a scale parameter. This model is then easily generalized to include x-y shift and rotation parameters. By integrating the scale-space model into a non-linear least squares method, the method can iteratively estimate the transformation (x-y shift, rotation, and scale) in an accurate and efficient manner. We compare our proposed scale-space integrated Lucas-Kanade's method (SILK) against Lucas-Kanade's optical flow and scale-invariant feature transform (SIFT) matching and show that our method is suitable for super-resolution from very low resolution image sequences.