The presence of motion blur is unavoidable in hand-held cameras, especially in low-light conditions. In this paper, we address the inverse rendering problem of estimating the latent image, scene depth and camera motion from a set of differently blurred images of the scene. Our framework can account for depth variations, non-uniform motion blur as well as mis-alignments in the captured observations. We initially describe an iterative algorithm to estimate ego motion in 3D scenes by suitably harnessing the point spread functions across the blurred images at different spatial locations. This is followed by recovery of latent image and scene depth by alternate minimization. © 2015 IEEE.

A. N. Rajagopalan

Department of Electrical Engineering

M. S. Arun

Algorithms

Cameras

Computer graphics

Computer vision

Estimation

image segmentation

Inverse problems

Iterative methods

Niobium

Optical transfer function

Three dimensional computer graphics

Depth variation

HAND-HELD CAMERAS

INVERSE RENDERING

Iterative algorithm

LOW LIGHT CONDITIONS

NON-UNIFORM MOTIONS

Spatial location

THREE-DIMENSIONAL DISPLAY

RENDERING (COMPUTER GRAPHICS)

IIT Madras is a public technical and research university located in Chennai, Tamil Nadu. Founded in 1959, it is recognised as an Institute of National Importance.

IIT Madras has been ranked as the top engineering institute in India for four years in a row by the National Institutional Ranking Framework of the MHRD

It currently offers undergraduate, postgraduate and research degrees across 16 disciplines in Engineering, Sciences, Humanities and Management. About 596 faculty belonging to science and engineering departments and centres of the Institute are engaged in teaching, research and industrial consultancy.

IIT Madras

Multi-Shot Deblurring for 3D Scenes

Proceedings of the IEEE International Conference on Computer Vision

Motion blur due to camera shake is a common occurrence. During image capture, the apparent motion of a scene point in the image plane varies according to both camera motion and scene structure. Our objective is to infer the camera motion and the depth map of static scenes using motion blur as a cue. To this end, we use an unblurred-blurred image pair. Initially, we develop a technique to estimate the transformation spread function (TSF) which symbolizes the camera shake. This technique uses blur kernels estimated at different points across the image. Based on the estimated TSF, we recover the complete depth map of the scene within a regularization framework. © 2013 Springer Science+Business Media New York.

International Journal of Computer Vision

Shape from sharp and motion-blurred image pair

We address the problem of estimating the latent image of a static bilayer scene (consisting of a foreground and a background at different depths) from motion blurred observations captured with a handheld camera. The camera motion is considered to be composed of in-plane rotations and translations. Since the blur at an image location depends both on camera motion and depth, deblurring becomes a difficult task. We initially propose a method to estimate the transformation spread function (TSF) corresponding to one of the depth layers. The estimated TSF (which reveals the camera motion during exposure) is used to segment the scene into the foreground and background layers and determine the relative depth value. The deblurred image of the scene is finally estimated within a regularization framework by accounting for blur variations due to camera motion as well as depth. © 2013 IEEE.

Fulltext

Proceedings of the IEEE Computer Society Conference on Computer  Vision and Pattern Recognition

Journal	Data powered by TypesetProceedings of the IEEE International Conference on Computer Vision
Publisher	Data powered by TypesetInstitute of Electrical and Electronics Engineers Inc.
ISSN	15505499
Open Access	No