Spatio-angular resolution tradeoffs in integral photography

By Todor Georgiev, K. Zheng, Brian Curless, David Salesin, Shree K. Nayar, C. Intwala. Eurographics Symposium on Rendering, 2006

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Light Field Image Processing: An Overview (2017, methodology): “[30] employed a segmentation-based optical flow method to compute the flow between an image and two neighboring views.”
The focused plenoptic camera (2009, methodology): “Given: Discrete Nx ×Ny × nx × ny radiance r[i,j,k,l] Output: Rendered M ∗Nx ×M ∗Ny image I[s,t] For (s,t) in (M*Nx, M*Ny) I[s,t] = ∑ nx,ny r[i,j,k,l], where i=(s/Nx)*M, j=(t/Ny)*M, k=(s%Nx)*M, and l=(t%Ny)*M, In practice, to mitigate the effects of poor image quality at the edges of microlens images”
Learning-based view synthesis for light field cameras (2016, methodology): “Generally, they are obtained by capturing a set of 2D images from different views [Levoy and Hanrahan 1996; Wilburn et al. 2005] or using a microlens array [Adelson and Wang 1992; Ng et al. 2005; Georgiev et al. 2006].”
Image and depth from a conventional camera with a coded aperture (2007, background): “Multiple viewpoints can also be collected in a single image using a plenoptic camera [Adelson and Wang 1992; Ng et al. 2005; Georgiev et al. 2006; Levoy et al. 2006] but at the price of a significant loss in the spatial resolution of the image.”
Dappled photography: mask enhanced cameras for heterodyned light fields and coded aperture refocusing (2007, methodology): “[1997] place an array of positive lenses (aided by prisms in [Georgiev et al. 2006]) in front of a conventional camera.”