Implicit Epipolar Geometric Function Based Light Field Continuous Angular Representation

Abstract

Light field plays an important role in many different applications such as virtual reality, microscopy and computational photography. However, low angular resolution limits the further application of light field. The existing state of the art light field angular super-resolution reconstruction methods can only achieve limited fixed-scale angular super-resolution. This paper focuses on a continuous arbitrary-scale light field angular super-resolution via introducing the implicit neural representation into the light field two-plane parametrization. Specifically, we first formulate a 4D implicit epipolar geometric function for light field continuous angular representation. Considering it is difficult and inefficient to directly learn this 4D implicit function, a divide-and-conquer learning strategy and a spatial information embedded encoder are then proposed to convert the 4D implicit function learning into a joint learning of 2D local implicit functions. Furthermore, we design a special epipolar geometric convolution block (EPIBlock) to encode the light field epipolar constraint information. Experiments on both synthetic and real-world light field datasets demonstrate that our method exhibits not only significant superiority in fixed-scale angular super-resolution, but also achieves arbitrary high magnification light field super-resolution while still maintaining the clear light field epipolar geometric structure.