Light Field Interpolation Using Structured Image Arrays
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Solution Overview
Problem
Traditional digital media formats, such as 2D flat images, limit the ability to recreate memories and events with high fidelity and require significant additional data for interpolation or extrapolation, leading to inefficiencies in processing speed and storage, especially when trying to generate 3D images or models.
Innovation Solution
A method for interpolating images from a multi-directional structured image array using a camera to obtain overlapping images, determining candidate transformations, and blending pixel values to generate artificially rendered images, allowing for efficient interpolation and extrapolation without the need for dense depth maps or optical flow data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional interpolation or extrapolation methods are used to generate 3D images or models, then image quality and fidelity are improved, but data requirements increase significantly and processing speed decreases
Solution Approach 1:
The patent extracts and utilizes only the essential geometric information needed for interpolation from the multi-directional image array, rather than requiring complete dense depth maps or optical flow data for every pixel. This selective extraction reduces data requirements while maintaining sufficient precision for generating intermediate views.
Solution Approach 2:
The patent introduces a light field representation as an intermediary data structure that organizes multi-directional images in a structured array format. This intermediary representation enables efficient interpolation by providing a natural framework for sampling and combining views without requiring additional dense depth information.
2Measurement precision
If dense depth maps or optical flow maps are used for interpolation, then interpolation accuracy is improved, but processing speed and transfer rates decrease
Solution Approach 1:
The patent segments the scene into multiple discrete views captured from different directions and organizes them in a structured light field array. This segmentation allows independent processing of each view and enables selective combination of relevant views for interpolation, improving processing efficiency compared to processing dense per-pixel depth or flow data.
Solution Approach 2:
The patent transitions from traditional 2D image processing to 4D light field processing by adding two spatial dimensions (horizontal and vertical angular directions). This dimensional expansion provides geometric constraints that improve interpolation accuracy while the structured organization enables efficient algorithms that process only necessary data.
3Adaptability or versatility
If multiple images with overlapping subject matter are processed, then three-dimensional modeling capability is improved, but computational complexity increases
Solution Approach 1:
The patent creates a universal light field data structure that serves multiple functions: it enables 3D modeling, supports interpolation to generate intermediate views, allows for virtual camera positioning, and facilitates various rendering operations. This multi-functional framework reduces overall computational complexity by providing a unified representation rather than requiring separate processing pipelines for different operations.
Data Source
AI summary
This present disclosure relates to systems and processes for interpolating images of an object from a multi-directional structured image array. In particular embodiments, a plurality of images corresponding to a light field is obtained using a camera. Each image contains at least a portion of overlapping subject matter with another image. First, second, and third images are determined, which are the closest three images in the plurality of images to a desired image location in the light field. A first set of candidate transformations is identified between the first and second images, and a second set of candidate transformations is identified between the first and third images. For each pixel location in the desired image location, first and second best pixel values are calculated using the first and second set of candidate transformations, respectively, and the first and second best pixel values are blended to form an interpolated pixel.