Saleh Mosaddegh

Joint demosaicking and denoising by total variation minimization

Joint demosaicking and denoising consists in reconstructing a color image from the noisy raw data output by the sensor of a digital camera. We adopt a variational formulation in which the reconstructed image has minimal total variation under the constraint of consistency with the available measurements. This way, the recovered color image has smooth chrominance but the sharp edges are maintained and the noise is transferred to the luminance channel. This channel is denoised subsequently.

Short baseline line matching for central imaging systems

We develop a generic line matching method especially applicable to omnidirectional images taken from constructed scenes with short baseline motion where the motion of the imaging system between two views is mainly an arbitrary rotation and the translation of the camera between two views with respect to its distance to the imaged scene is negligible. We start by studying the relationship between images of lines on unitary sphere followed by proposing a simple algorithm for simultaneously matching vanishing points and lines. The developed algorithm is very simple, yet it works on images captured by all types of central imaging systems, including perspective, fish-eye and catadioptric images. Various experimental results on both synthetic and real images taken by different central cameras as well as an application of the algorithm for creating high resolution panoramic images from several high resolution perspective images are also presented.

Adaptive processing of catadioptric images using polarization imaging: towards a pola-catadioptric model

Abstract. A nonparametric method to define a pixel neighborhood in catadioptric images is presented. The method is based on an accurate modeling of the mirror shape by mean of polarization imaging. Unlike most processing methods existing in the literature, this method is nonparametric and enables us to respect the catadioptric image’s anamorphosis. The neighborhood is directly derived from the two polarization parameters: the angle and the degree of polarization. Regardless of the shape of the catadioptric sensor’s mirror (including noncentral configurations), image processing techniques such as image derivation, edge detection, interest point detection, as well as image matching, can be efficiently performed.

Line based motion estimation and reconstruction of piece-wise planar scenes

We present an algorithm for reconstruction of piece-wise planar scenes from only two views and based on minimum line correspondences. We first recover camera rotation by matching vanishing points based on the methods already exist in the literature and then recover the camera translation by searching among a family of hypothesized planes passing through one line. Unlike algorithms based on line segments, the presented algorithm does not require an overlap between two line segments or more that one line correspondence across more than two views to recover the translation and achieves the goal by exploiting photometric constraints of the surface around the line. Experimental results on real images prove the functionality of the algorithm.

A Generic Method of Line Matching for Central Imaging Systems under Short-Baseline Motion

Line matching across images taken by a central imaging system (perspective or catadioptric) with focus on short baseline motion of the system is proposed. The relationship between images of lines on unitary sphere is studied and a simple algorithm for matching lines are proposed assuming the rotation of the system is known apriori or it can be estimated from some correspondences in two views. Two methods are discussed for retrieving R in the case it is not known apriori. Experimental results on both synthetic and real images are also presented.

Adapted processing of catadioptric images using polarization imaging

A non parametric method that defines a pixel neighborhood within catadioptric images is presented in this paper. It is based on an accurate modeling of the mirror shape by using polarization imaging. Unlike the most of current processing methods in the literature, this method is non-parametric and can deal with the deformation of catadioptric images. This paper demonstrates how an appropriate neighborhood can be derived from the polarization parameters by estimation of the degree of polarization and the angle of polarization which in return directly provide an adapted neighborhood of each pixel that can be used to perform image derivation, edge detection, interest point detection and namely image matching, all in the catadioptric image plane.

Ego-Translation Estimation from One Straight Edge in Constructed Scenes

The task of recovering the camera motion relative to the environment (ego-motion estimation) is fundamental to many computer vision applications and this field has witnessed a wide range of approaches to this problem. Usual approaches are based on point or line correspondences, optical flow or the so-called direct methods. We present an algorithm for determining 3D motion and structure from one line correspondence between two perspective images. Classical methods which use supporting lines need at least three images. In this work, however, we show that only one supporting line correspondence belong to a planar surface in the space is enough to estimate the camera ego-translation provided the texture on the surface close to the line is enough discriminative. Only one line correspondence is enough and it is not necessary that two matched line segments contain the projection of a common part of the corresponding line segment in space. We first recover camera rotation by matching vanishing points based on the methods already exist in the literature and then recovering the camera translation. Experimental results on both synthetic and real images prove the functionality of the proposed method.

Motion estimation and reconstruction of piecewise planar scenes from two views

The task of recovering the camera motion relative to the environment (motion estimation) is fundamental to many computer vision applications. We present an algorithm for reconstruction of piece-wise planar scenes from only two views and based on minimum line correspondences. We first recover camera rotation by matching vanishing points based on the methods already exist in the literature and then recover the camera translation by searching among a family of hypothesized planes passing through one line. Unlike algorithms based on line segments, the presented algorithm does not require an overlap between two line segments or more than one line correspondence across more than two views to recover the translation and achieves the goal by exploiting photometric constraints of the surface around the line. Experimental results on real images prove the functionality of the algorithm.