Deep Image Resampling via 3D Region Partitioning
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Solution Overview
Problem
Conventional methods of upsampling deep images involve flattening them into flat images, which results in the loss of depth-specific channel information, complicating later image processing techniques.
Innovation Solution
The technique involves receiving a deep image, partitioning it into three-dimensional (3D) regions defined by planar surfaces with associated depth values, and generating an upsampled image while interpolating new channel sample values to preserve depth-specific geometric information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If conventional flattening techniques are used to simplify upsampling, then the upsampling process becomes easier and more straightforward, but depth-specific channel information is lost
Solution Approach 1:
The patent transitions from conventional 2D image processing to 3D deep image processing by introducing depth values as a third dimension. Each pixel is represented with multiple channel sample values, each having an associated depth value z, transforming the data structure from flat 2D arrays to 3D data spaces that preserve spatial depth information throughout the upsampling process.
Solution Approach 2:
The patent segments the deep image processing into distinct operational phases: partitioning the deep image into 3D regions defined by planar surfaces, performing upsampling within each region while preserving depth relationships, and then reconstructing the output. This segmentation allows complex 3D upsampling to be broken down into manageable steps that maintain depth information.
2Adaptability or versatility
If depth information is preserved during upsampling, then later image processing techniques can effectively handle volumetric elements, but the upsampling process becomes more complex
Solution Approach 1:
The patent performs preliminary partitioning of the deep image into 3D regions before executing the upsampling operation. By pre-defining planar surfaces and depth boundaries, the system prepares the data structure in advance, making the subsequent upsampling process more systematic and manageable despite the inherent complexity of 3D processing.
Solution Approach 2:
The patent introduces 3D regions defined by planar surfaces as intermediary structures that facilitate the upsampling process. These regions act as mediators between the input deep image and output deep image, providing a structured framework for preserving depth information while enabling the upsampling operation to proceed in a controlled manner.
Data Source
AI summary
The present invention sets forth a technique for performing resampling of a deep image. The technique includes receiving a deep image, where the deep image includes one or more channel sample values, each channel sample value including one or more associated depth values and partitioning the deep image into one or more three-dimensional (3D) regions, wherein each 3D region is defined by one or more planar surfaces each including an associated depth value. The technique also includes generating an upsampled image having a greater resolution than the deep image and interpolating, based on a specified interpolation technique, one or more new channel sample values associated with pixels included in the upsampled image. The technique further includes generating an output deep image based on the upsampled image and the one or more new channel sample values.