Digital Image Patch Matching with Cross-Resolution Nearest Neighbor Fields
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Solution Overview
Problem
Conventional digital image editing systems require excessive processing power, memory, and computing time due to inefficient and rigid patch matching operations, especially when dealing with high-resolution panoramic images, and are inflexible in applying patch matching operations across different digital image versions or channels.
Innovation Solution
A patch matching system that initializes patch matching operations using a nearest neighbor field from a smaller resolution image to generate patches for a larger resolution image, reducing computational expense by avoiding analysis at scales below a target scale and utilizing a quantization technique to compress nearest neighbor fields.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems analyze digital images at multiple scales to generate matching patches, then patch matching accuracy is improved, but processing power and computing time increase excessively
Solution Approach 1:
The system performs patch matching analysis on a first (lower resolution) digital image before processing the second (higher resolution) image. The nearest neighbor field computed from the first image serves as a preliminary result that initializes or guides the patch matching for the second image, avoiding redundant analysis at coarse scales and reducing overall computational energy consumption while maintaining matching accuracy
Solution Approach 2:
The processing is segmented into two stages: first processing a downsampled or lower resolution version of the image to obtain a nearest neighbor field, then using this field to guide the processing of the full-resolution image. This segmentation allows the computationally expensive multi-scale analysis to be performed only where necessary (at full resolution) rather than redundantly across all scales
2Manufacturing precision
If conventional systems process high-resolution panoramic images with multiple patches, then image quality is maintained, but memory requirements and computing time compound significantly
Solution Approach 1:
The system computes the nearest neighbor field on a first digital image (which may be a downsampled version or a previously processed version) before processing the second high-resolution panoramic image. This preliminary computation establishes a foundation that accelerates the subsequent processing of the high-resolution image, reducing the compounded computing time that would otherwise result from processing each patch independently at full resolution
Solution Approach 2:
The nearest neighbor field computed from the first image serves as a template or guide that can be applied or adapted to the second high-resolution image. Rather than performing complete patch matching analysis from scratch on the large panoramic image, the system leverages the pre-computed field structure, effectively copying the matching pattern and adapting it to the higher resolution data
3Adaptability or versatility
If conventional systems apply patch matching operations serially to different digital image versions, then comprehensive editing is achieved, but system flexibility and efficiency decrease
Solution Approach 1:
The system processes multiple digital images (first and second images, which may represent different versions, resolutions, or channels) using a unified nearest neighbor field computation approach. The same patch matching algorithm and nearest neighbor field structure are universally applied across different image types and processing stages, enabling flexible editing operations on various image versions while improving efficiency by avoiding redundant analysis
Solution Approach 2:
By computing the nearest neighbor field on a first image version before processing subsequent image versions, the system establishes a reusable matching framework that can be applied across multiple editing operations. This preliminary computation enables the system to handle different image versions and channels more efficiently, as the fundamental patch matching structure is already determined
Data Source
AI summary
The present disclosure relates to systems, methods, and non-transitory computer readable media for generating modified digital images by utilizing a patch match algorithm to generate nearest neighbor fields for a second digital image based on a nearest neighbor field associated with a first digital image. For example, the disclosed systems can identify a nearest neighbor field associated with a first digital image of a first resolution. Based on the nearest neighbor field of the first digital image, the disclosed systems can utilize a patch match algorithm to generate a nearest neighbor field for a second digital image of a second resolution larger than the first resolution. The disclosed systems can further generate a modified digital image by filling a target region of the second digital image utilizing the generated nearest neighbor field.