Now showing 1 - 10 of 10
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    Depth from motion and optical blur with an unscented Kalman filter
    (01-05-2012)
    Paramanand, C.
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    Space-variantly blurred images of a scene contain valuable depth information. In this paper, our objective is to recover the 3-D structure of a scene from motion blur/optical defocus. In the proposed approach, the difference of blur between two observations is used as a cue for recovering depth, within a recursive state estimation framework. For motion blur, we use an unblurred-blurred image pair. Since the relationship between the observation and the scale factor of the point spread function associated with the depth at a point is nonlinear, we propose and develop a formulation of unscented Kalman filter for depth estimation. There are no restrictions on the shape of the blur kernel. Furthermore, within the same formulation, we address a special and challenging scenario of depth from defocus with translational jitter. The effectiveness of our approach is evaluated on synthetic as well as real data, and its performance is also compared with contemporary techniques. © 1992-2012 IEEE.
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    Deskewing of underwater images
    (01-03-2015)
    Seemakurthy, Karthik
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    We address the problem of restoring a static planar scene degraded by skewing effect when imaged through a dynamic water surface. In particular, we investigate geometric distortions due to unidirectional cyclic waves and circular ripples, phenomena that are most prevalent in fluid flow. Although the camera and scene are stationary, light rays emanating from a scene undergo refraction at the fluid-air interface. This refraction effect is time varying for dynamic fluids and results in nonrigid distortions (skew) in the captured image. These distortions can be associated with motion blur depending on the exposure time of the camera. In the first part of this paper, we establish the condition under which the blur induced due to unidirectional cyclic waves can be treated as space invariant. We proceed to derive a mathematical model for blur formation and propose a restoration scheme using a single degraded observation. In the second part, we reveal how the blur induced by circular ripples (though space variant) can be modeled as uniform in the polar domain and develop a method for deskewing. The proposed methods are tested on synthetic as well as real examples.
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    Efficient change detection for very large motion blurred images
    (24-09-2014)
    Rengarajan, Vijay
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    Punnappurath, Abhijith
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    Seetharaman, Guna
    In this paper, we address the challenging problem of registration and change detection in very large motion blurred images. The unreasonable demand that this task puts on computational and memory resources precludes the possibility of any direct attempt at solving this problem. We address this issue by observing the fact that the camera motion experienced by a sufficiently large sub-image is approximately the same as that of the entire image itself. We devise an algorithm for judicious sub-image selection so that the camera motion can be deciphered correctly, irrespective of the presence or absence of occluder. We follow a reblur-difference framework to detect changes as this is an artifact-free pipeline unlike the traditional deblur-difference approach. We demonstrate the results of our algorithm on both synthetic and real data.
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    Camera Shutter-Independent Registration and Rectification
    (01-04-2018)
    Vasu, Subeesh
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    Seetharaman, Guna
    Inevitable camera motion during exposure does not augur well for free-hand photography. Distortions introduced in images can be of different types and mainly depend on the structure of the scene, the nature of camera motion, and the shutter mechanism of the camera. In this paper, we address the problem of registering images taken from global shutter and rolling shutter cameras and reveal the constraints on camera motion that admit registration, change detection, and rectification. Our analysis encompasses degradations arising from camera motion during exposure and differences in shutter mechanisms. We also investigate conditions under which camera motions causing distortions in reference and target image can be decoupled to yield the underlying latent image through RS rectification. We validate our approach using several synthetic and real examples.
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    Motion blur for motion segmentation
    (01-01-2013)
    Paramanand, C.
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    In this paper, we develop a method for motion segmentation using blur kernels. A blur kernel represents the apparent motion undergone by a scene point in the image plane. When the relative motion between the camera and scene is not restricted to fronto-parallel translations, the shape of the blur kernels can vary across image points. For a dynamic scene, we effectively model motion blur using transformation spread functions (TSFs) which represent the relative motions. Given a set of blur kernels that are estimated at different points across an image, we develop a method to segment them according to their relative motion. We initially group the blur kernels based on their 'compatibility'. We refine this initial segmentation by jointly estimating the TSF and removing the outliers. © 2013 IEEE.
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    Restoration of foggy and motion-blurred road scenes
    (01-01-2013)
    Veeramani, Thangamani
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    Seetharaman, Guna
    Existing single image defogging techniques can restore contrast loss and yield a rough estimate of the depth map of a scene. The ubiquity of hand-held imaging devices has attracted considerable attention to motion blur but this has not been addressed in the context of images captured under foggy conditions. In this paper, we show how to restore foggy motion-blurred images using depth cues derived from fog itself. Initially, we address restoration of images blurred primarily due to in-plane translational camera motion. This is followed by a scheme for handling general camera motion blur with a projective blur model. We demonstrate that foggy road scene images can be segmented into road, left, right and sky planes, and that each of these planes can be deblurred individually. © 2013 IEEE.
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    Non-uniform deblurring in hdr image reconstruction
    (09-09-2013)
    Vijay, Channarayapatna Shivaram
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    Paramanand, Chandramouli
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    Chellappa, Rama
    Hand-held cameras inevitably result in blurred images caused by camera-shake, and even more so in high dynamic range imaging applications where multiple images are captured over a wide range of exposure settings. The degree of blurring depends on many factors such as exposure time, stability of the platform, and user experience. Camera shake involves not only translations but also rotations resulting in nonuniform blurring. In this paper, we develop a method that takes input non-uniformly blurred and differently exposed images to extract the deblurred, latent irradiance image. We use transformation spread function (TSF) to effectively model the blur caused by camera motion. We first estimate the TSFs of the blurred images from locally derived point spread functions by exploiting their linear relationship. The scene irradiance is then estimated by minimizing a suitably derived cost functional. Two important cases are investigated wherein 1) only the higher exposures are blurred and 2) all the captured frames are blurred. © 1992-2012 IEEE.
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    Harnessing motion blur to uncover splicing
    (01-01-2013)
    Rao, M. Purnachandra
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    Image tampering has become rampant in today's world due to availability of sophisticated image editing tools. In this paper, we deal with the problem of image splicing which is one form of tampering. We propose a passive method to detect the presence of splicing in a given image based on inconsistencies derived from motion blur. Both planar and 3D scenes are considered. The cause of blurring in the image is restricted to translation camera motion while the scene is assumed to be static. We validate our approach on synthetic as well as real examples. © 2013 IEEE.
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    Harnessing motion blur to unveil splicing
    (01-04-2014)
    Rao, Makkena Purnachandra
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    Seetharaman, Guna
    The extensive availability of sophisticated image editing tools has rendered it relatively easy to produce fake images. Image splicing is a form of tampering in which an original image is altered by copying a portion from a different source. Because the phenomenon of motion blur is a common occurrence in hand-held cameras, we propose a passive method to automatically detect image splicing using blur as a cue. Specifically, we address the scenario of a static scene in which the cause of blur is due to hand shake. Existing methods for dealing with this problem work only in the presence of uniform space-invariant blur. In contrast, our method can expose the presence of splicing by evaluating inconsistencies in motion blur even under space-variant blurring situations. We validate our method on several examples for different scene situations and camera motions of interest. © 2014 IEEE.
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    Image Registration and Change Detection under Rolling Shutter Motion Blur
    (01-10-2017)
    Rengarajan, Vijay
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    Seetharaman, Guna
    In this paper, we address the problem of registering a distorted image and a reference image of the same scene by estimating the camera motion that had caused the distortion. We simultaneously detect the regions of changes between the two images. We attend to the coalesced effect of rolling shutter and motion blur that occurs frequently in moving CMOS cameras. We first model a general image formation framework for a 3D scene following a layered approach in the presence of rolling shutter and motion blur. We then develop an algorithm which performs layered registration to detect changes. This algorithm includes an optimisation problem that leverages the sparsity of the camera trajectory in the pose space and the sparsity of changes in the spatial domain. We create a synthetic dataset for change detection in the presence of motion blur and rolling shutter effect covering different types of camera motion for both planar and 3D scenes. We compare our method with existing registration methods and also show several real examples captured with CMOS cameras.