Three Phases of Computational Photography by Ramesh Raskar
Three Phases of Computational Photography by Ramesh Raskar
1. Epsilon Photography It is about building a camera that has enhanced performance in terms of the traditional parameters, such as dynamic range, field of view, or depth of field. The scene is recorded via multiple images, each captured by epsilon variation of the parameters and merged into an image that has best features from all of them. It corresponds to the low-level vision: estimating pixels and pixel features.
– Low-level Vision: Pixels – Multi photos by bracketing (HDR, panorama) – ‘Ultimate camera’
2. Coded Photography It is about building a camera that go beyond capabilities of the best possibilities of conventional camera. It reversibly encodes information about the scene in a single (or a few) photograph so that the corresponding decoding allows powerful decomposition of the image into light fields, motion deblurred images, global/direct illumination components or distinction between geometric versus material discontinuities. This corresponds to the mid-level vision: segmentation, organization, inferring shapes, materials and edges.
– Mid-Level Cues: Regions, Edges, Motion, Direct/global – Single/few snapshot: Reversible encoding of data – Additional sensors/optics/illumination
3. Essence Photography It is about going beyond the radiometric quantities and challenging the notion that a camera should mimic a single-chambered human eye. Instead of recovering physical parameters, the goal will be to capture the visual essence of the scene and analyze the perceptually critical components. It may loosely resemble depiction of the world after high level vision processing. It will spawn new forms of visual artistic expression and communication.
– Not mimic human eye – Beyond single view/illumination – ‘New artform'
Read more Examples of the three phases Paper on Epsilon Photography and Coded Photography
