Real-time deconvolution of light fields through pixel selection in the point-spread-function and direct inversion of the image formation process

This paper explores the optimization of light field deconvolution, a key process in image processing that reconstructs a 3D object space or a 2D refocus plane from a light field. Despite the critical role of deconvolution in light field technology, existing methods are often slow, computationally intensive, and unsuitable for real-time processing. Existing algorithms, such as the Richardson-Lucy approach, while groundbreaking, still suffer performance limitations due to their iterative nature and high computational costs. Central to our approach is the strategic selection of influential pixels within the point-spread-function, reducing redundant computations by focusing only on pixels contributing to a significant portion of the point-spread-function's total intensity. In addition, we explore the potential to directly invert the image formation model, bypass iterative computations, and further accelerate the deconvolution process. Our findings reveal notable improvements in computational efficiency, with some of our methods achieving real-time performance. The reconstruction quality, measured using metrics such as the mean squared error, remained comparable to existing approaches, indicating a favorable balance between speed and reconstruction quality.