A N Rajagopalan

We propose a new method that extends the capability of shape-from-focus (SFF) to estimate the depth profile of 3-D objects in the presence of structure-dependent pixel motion. Existing SFF techniques work under the constraint that there is no parallax in the captured stack of frames. However, in off-the-shelf cameras, there can be appreciable pixel motion among the observations when there is relative motion between the object and the camera. In such a scenario, the depth estimates will be erroneous if the parallax effect is not factored in. Our degradation model accounts for pixel migration effects in the observations due to parallax resulting in a generalization of the SFF technique. We show that pixel motion and defocus blur therein are tightly coupled to the underlying shape of the 3-D object. Simultaneous reconstruction of the underlying 3-D structure and the all-in-focus image is carried out within an optimization framework using local image operations. The proposed method when tested on many examples, both synthetic and real, is very effective and delivers state-of-the-art performance. © 2011 IEEE.

Shape from focus (SFF) estimates the structure of a 3D object using the degree of focus as a cue in a sequence of observations. The estimate of the depth profile is however, vulnerable to lack of sufficient scene texture. In this paper, we propose a method to improve the estimate of the structure of the object by exploiting neighbourhood dependencies. A degradation model is used to describe the formation of space-variantly blurred observations in SFF. The shape of the object is modeled as a Markov random field and a suitably derived objective function is minimized to arrive at the final estimate of the shape.

This paper presents a novel approach based on the unscented Kalman filter (UKF) for image estimation in film-grain noise. The image prior is modeled as non-Gaussian and is incorporated within the UKF frame work using importance sampling. A small carefully chosen deterministic set of sigma points is used to capture the prior and is propagated through film-grain nonlinearity to compute image statistics. Experimental results are given to demonstrate the efficacy of the proposed method. © 2007 IEEE.

Range images often suffer from issues such as low resolution (LR) (for low-cost scanners) and presence of missing regions due to poor reflectivity, and occlusions. Another common problem (with high quality scanners) is that of long acquisition times. In this work, we propose two approaches to counter these shortcomings. Our first proposal which addresses the issues of low resolution as well as missing regions, is an integrated super-resolution (SR) and inpainting approach. We use multiple relatively-shifted LR range images, where the motion between the LR images serves as a cue for super-resolution. Our imaging model also accounts for missing regions to enable inpainting. Our framework models the high resolution (HR) range as a Markov random field (MRF), and uses inhomogeneous MRF priors to constrain the solution differently for inpainting and super-resolution. Our super-resolved and inpainted outputs show significant improvements over their LR/interpolated counterparts. Our second proposal addresses the issue of long acquisition times by facilitating reconstruction of range data from very sparse measurements. Our technique exploits a cue from segmentation of an optical image of the same scene, which constrains pixels in the same color segment to have similar range values. Our approach is able to reconstruct range images with as little as 10% data. We also study the performance of both the proposed approaches in a noisy scenario as well as in the presence of alignment errors. © 2011 Elsevier Inc. All rights reserved.

This paper presents discontinuity adaptive image estimation within the Kalman filter framework by non-Gaussian modeling of the image prior. A generalized methodology is proposed for specifying state-dynamics using the conditional density of the state given its neighbors, without explicitly defining the state equation. The novelty of our approach lies in directly obtaining the predicted mean and variance of the non-Gaussian state conditional density by importance sampling and incorporating them in the update step of the Kalman filter. Experimental results are given to demonstrate the effectiveness of the proposed method in preserving edges. © 2007 IEEE.

Under stereo settings, the twin problems of image superresolution (SR) and high-resolution (HR) depth estimation are intertwined. The subpixel registration information required for image superresolution is tightly coupled to the 3D structure. The effects of parallax and pixel averaging (inherent in the downsampling process) preclude a priori estimation of pixel motion for superresolution. These factors also compound the correspondence problem at low resolution (LR), which in turn affects the quality of the LR depth estimates. In this paper, we propose an integrated approach to estimate the HR depth and the SR image from multiple LR stereo observations. Our results demonstrate the efficacy of the proposed method in not only being able to bring out image details but also in enhancing the HR depth over its LR counterpart. © 2010 IEEE.