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Abstract. We present a novel method for solving blind deconvolution, i.e., the task of recovering a sharp image given a blurry one. We focus on blurry images obtained from a coded aperture camera, where both the camera and the scene are static, and allow blur to vary across the image domain. As most methods for blind deconvolution, we solve the problem in two steps: First, we estimate the coded blur scale at each pixel; second, we deconvolve the blurry image given the estimated blur. Our approach is to use linear high-order priors for texture and second-order priors for the blur scale map, i.e., constraints involving two pixels at a time. We show that by incorporating the texture priors in a least-squares energy minimization we can transform the initial blind deconvolution task in a simpler optimization problem. One of the striking features of the simplified optimization problem is that the parameters that define the functional can be learned offline directly from natural images via singular value decomposition. We also show a geometrical interpretation of image blurring and explain our method from this viewpoint. In doing so we devise a novel technique to design optimally coded apertures. Finally, our coded blur identification results in computing convolutions, rather than deconvolutions, which are stable operations. We will demonstrate in several experiments that this additional stability allows the method to deal with large blur. We also compare our method to existing algorithms in the literature and show that we achieve state-of-the-art performance with both synthetic and real data.

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In this paper we present an algorithm for depth estimation from a monocular video sequence containing moving and deformable objects. The method is based on a coded aperture system (i.e., a conventional camera with a mask placed on the main lens) and it takes a coded video as input to provide a sequence of dense depth maps as output. To deal with nonrigid deformations, our work builds on the state-of-theart single-image depth estimation algorithm. Since singleimage depth estimation is very ill-posed, we cast the reconstruction task as a regularized algorithm based on nonlocalmeans filtering applied to both the spatial and temporal domain. Our assumption is that regions with similar texture in the same frame and in neighbouring frames are likely to belong to the same surface. Moreover, we show how to increase the computational efficiency of the method. The proposed algorithm has been successfully tested on challenging real scenarios.

Three-dimensional (3-D) imaging of the eye fundus, and in particular of the optic disc, is widely used to assess glaucoma progression over time. In the literature, 3-D images of the optic disc have been obtained from stereo and monocular fundus cameras. While stereo systems are the gold standard for optic disc examination, monocular systems are less expensive, and therefore of more practical use. This stimulated a thorough investigation of the limits and advantages of these two imaging modalities. Our conclusion is that monocular imaging is generally not suitable for 3-D estimation. This is attributed to the fact that monocular systems do not allow a change in the vantage point from which the retinal surface is observed, despite variations in the relative pose between the eye and the fundus camera. To validate this analysis we carry out several experiments on both stereo and monocular fundus cameras with standard 3-D reconstruction algorithms. Furthermore, we devise a calibration procedure to quantify experimentally the highest accuracy achievable with a stereo system.