Computational Photography
Computational photography combines plentiful computing, digital sensors, modern optics, actuators, and smart lights to escape the limitations of traditional film cameras and enables novel imaging applications. Unbounded dynamic range, variable focus, resolution, and depth of field, hints about shape, reflectance, and lighting, and new interactive forms of photos that are partly snapshots and partly videos are just some of the new applications found in Computational Photography.
Background & Objective: In traditional film-like digital photography, camera images represent a view of the scene via a 2D array of pixels. Computational Photography attempts to understand and analyze a ray-based representation of the scene. The camera optics encode the scene by bending the rays, the sensor samples the rays over time, and the final 'picture' is decoded from these encoded samples. The lighting (scene illumination) follows a similar path from the source to the scene via optional spatio-temporal modulators and optics. In addition, the processing may adaptively control the parameters of the optics, sensor and illumination.
Technical Discussion: There are four elements of Computational Photography: (i) Generalized Optics; (ii) Generalized Sensor; (iii) Processing; and (iv) Generalized Illumination. The first three form the Computational Camera. Like other imaging fields, in addition to these geometry defining elements, Computational Photography deals with other dimensions such as time, wavelength and polarization.
Ongoing MERL projects on computational photography: 1) Coded Exposure (Flutter Shutter Camera), to handle motion deblurring; 2) Coded Aperture for extended depth of field, digital refocusing; 3) Coded Illumination (Multi-flash camera) for detecting depth edges in real time, useful for numerous vision applications; and 4) Heterodyne Light Field Camera using a transmissive mask placed close to the sensor: For extended depth of field and capturing 4D light field.
Outside Collaborations: Prof. Jack Tumblin, Dept of Computer Science, Northwestern University, and Prof. Shree Nayar, Columbia University, NY.
Contacts:
Ramesh Raskar
Amit Agrawal
Publications:
Agrawal, A.; Raskar, R., "Resolving Objects at Higher Resolution from a Single Motion-blurred Image", IEEE Computer Society Conference on Computer Vision & Pattern Recognition (CVPR), ISBN: 1-4244-1180-7, pp. 1-8, June 2007 (IEEE Xplore, TR2007-036)
Raskar, R.; Agrawal, A.; Tumblin, J., "Coded Exposure Photography: Motion Deblurring Using Fluttered Shutter", ACM Transactions on Graphics (TOG), ISSN: 0730-0301, Vol. 25, Issue 3, pp. 795-804, July 2006 (TR2006-124)
Technology Area: Imaging
Modification Date: September 12, 2007
