Sharath Venkatesha

Motion invariance and custom blur from lens motion

We demonstrate that image stabilizing hardware included in many camera lenses can be used to implement motion invariance and custom blur effects. Motion invariance is intended to capture images where objects within a range of velocities appear defocused with the same point spread function, obviating the need for blur estimation in advance of de-blurring. We show that the necessary parabolic motion can be implemented with stabilizing lens motion, but that the range of velocities to which capture is invariant decreases with increasing exposure time. We also show that, when that range is expanded through increased lens displacement, lens motion becomes less repeatable. In addition to motion invariance, we demonstrate that stabilizing lens motion can be used to design custom defocus kernels for aesthetic purposes, and can replace lens accessories.

Motion aware motion invariance

We address motion de-blurring using a computational camera that captures an image while the stabilizing optical element moves in a modified Canon IS lens. Our work builds on that of Levin et al. [11], who introduce parabolic motion as a means of achieving invariance to unknown subject velocity in an a priori known direction. While the previous work addresses a specific scenario - exact knowledge of motion orientation and a uniform, symmetric prior on its magnitude - we generalize this to address scenarios where the motion of objects in the scene or the camera itself are known to various extents. We describe a motion invariant camera based on an off-the-shelf lens, and show how its motion and position sensors can be used to inform both the image capture and de-blurring. We demonstrate that our changes to motion invariance improve the quality of captured images in the case of both object and camera motion.

Applications of multi-spectral imaging: failsafe industrial flame detector

Industrial and petrochemical facilities present unique challenges for fire protection and safety. Typical scenarios include detection of an unintended fire in a scene, wherein the scene also includes a flare stack in the background. Maintaining a high level of process and plant safety is a critical concern. In this paper, we present a failsafe industrial flame detector which has significant performance benefits compared to current flame detectors. The design involves use of microbolometer in the MWIR and LWIR spectrum and a dual band filter. This novel flame detector can help industrial facilities to meet their plant safety and critical infrastructure protection requirements while ensuring operational and business readiness at project start-up.

A Low-Noise Fluttering Shutter Camera Handling Accelerated Motion

We address the problem of motion blur removal using a computational camera with a fluttering shutter. While there are several prototype flutter shutter cameras, and many scenarios in which motion blur is problematic, there are few real-world uses of flutter shutter cameras due to two important limitations. The first is that the shutter mechanisms used to date - primarily Liquid Crystal Display (LCD) elements or electronic shutters - increase noise due to reduced light efficiency or multiple readouts, respectively. Secondly, the class of motions to which the flutter shutter is applicable has been limited to linear, constant velocity motion. We address the first limitation by developing a prototype flutter shutter camera with a reflective element providing high light efficiency and a single-read imaging system. In addition to improved noise performance, this method of exposure modulation imposes fewer limitations on the shutter sequence, allowing us to extend the flutter shutter technique to cases with constant (non-zero) acceleration. We demonstrate both the noise reduction and improved reconstructions in the case of an accelerating camera.

Computational cameras for moving iris recognition

Iris-based biometric identification is increasingly used for facility access and other security applications. Like all methods that exploit visual information, however, iris systems are limited by the quality of captured images. Optical defocus due to a small depth of field (DOF) is one such challenge, as is the acquisition of sharply-focused iris images from subjects in motion. This manuscript describes the application of computational motion-deblurring cameras to the problem of moving iris capture, from the underlying theory to system considerations and performance data.