Abstract
Light-field raw data captured by a state-of-the-art light-field camera is limited in its spatial and angular resolutions due to the camera's optical hardware. In this paper, we propose an all-software algorithm to synthesize light-field raw data from a single RGB-D input image, which is driven largely by the need in the research area of light-field data compression. Our synthesis algorithm consists of three key steps: (1) each pixel of the input image is regarded as a spot lighting source that emits directional light rays with an equal strength; (2) the optical path of each directional light ray through the camera's main lens as well as the corresponding micro lens is considered as accurately as possible; and (3) the occlusion of light rays among objects at different distances within the input image is handled with the depth information. The spatial and angular resolutions of our synthesized light-field data can be scaled up when the input RGB-D image has a higher and higher spatial resolution. Meanwhile, for a given input image with a fixed size, we pay a special attention to what would be the extreme we can push the parameters involved in our synthesis algorithm, such as the number of rays emitted from each pixel, the number of micro lenses, and the number of sensors associated with each micro lens. The usefulness of our synthesized data is validated by refocusing, all-in-focus, and sub-aperture reconstructions. In particular, all-in-focus images are evaluated objectively by computing the structural similarity (SSIM) index, which allows us to reach the goal of pushing to the extreme through selecting various parameters mentioned above.
Highlights
Light-field is a vector function that records the amount of light flowing in every direction through every point in the 3D space
This is accomplished by selecting three primary parameters involved in our synthesis algorithm, namely, the number of rays emitted from each pixel of the input image, the number of micro lens, and the number of sensors associated to each micro lens
In this paper, we have proposed an algorithm for synthesis of light-field raw data with a single RGB-D input image
Summary
Light-field is a vector function that records the amount of light flowing in every direction through every point in the 3D space. In order to develop and test the compression algorithm for high-resolution light-field data, we first need to access the light-field data with high resolution, which is impossible with the existing capturing devices To overcome this dilemma, we are motivated to develop a synthesis-based solution to provide light-field raw image data where the spatial resolution and angular resolution can be made arbitrarily high. Given an input image with a fixed resolution, an important goal is to push to the extreme in terms of the resolution (both spatial and angular) of the synthesized light-field raw data This is accomplished by selecting three primary parameters involved in our synthesis algorithm, namely, the number of rays emitted from each pixel of the input image, the number of micro lens, and the number of sensors associated to each micro lens.
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