Edge-Preserving Image Upsampling via Selective Guide Sampling
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Solution Overview
Problem
Traditional upsampling algorithms face challenges in maintaining sharpness and smoothness when resizing images, especially when the upsampling factor is large, often resulting in blurred or halo-defected images, and require significant computation.
Innovation Solution
A fast edge-preserving image upsampling method using selective sampling from a data image and one or more guide images, where the value of target pixels in the result image is determined by a weighted average of pixels from the data image based on guide images, allowing for sharpness preservation in edge areas and smoothness in non-edge areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Volume of moving object
If traditional upsampling algorithms are used, then the image size is increased, but the image quality deteriorates with blurring and halo defects
Solution Approach 1:
The patent applies different processing strategies to different regions of the image: edge-preserving filtering is applied specifically to edge regions while smooth interpolation is applied to non-edge regions. This local differentiation allows the algorithm to maintain sharp edges while avoiding artifacts in smooth areas, thereby improving overall image quality during upsampling.
Solution Approach 2:
The image is segmented into edge regions and non-edge regions based on gradient analysis. By identifying edges through gradient magnitude calculation and processing different regions separately with appropriate filtering strategies, the algorithm avoids applying uniform processing that causes blurring and halo effects across the entire image.
2Manufacturing precision
If edge preservation is applied during upsampling, then the image quality is improved, but the computation time increases significantly
Solution Approach 1:
Instead of applying computationally intensive edge-preserving filtering to the entire image, the algorithm applies it only to identified edge regions. By performing partial action only where necessary (at edges) and using simpler interpolation elsewhere, the computation time is significantly reduced while maintaining image quality where it matters most.
Solution Approach 2:
The algorithm performs local gradient analysis to identify edge regions and applies different processing intensity to different areas. Edge regions receive full edge-preserving processing while non-edge regions receive standard interpolation, thereby reducing overall computational burden while maintaining quality at critical locations.
3Volume of moving object
If a large upsampling factor is used, then the output image size is significantly increased, but the computational burden becomes daunting
Solution Approach 1:
The upsampling process is segmented into edge detection, edge region identification, and region-specific processing steps. By dividing the computational task into discrete stages and applying appropriate algorithms only where needed, the overall computational burden is managed even for large upsampling factors.
Solution Approach 2:
For large upsampling factors, the algorithm applies computationally intensive operations only to edge regions rather than the entire upsampled area. This partial action approach maintains essential image features while dramatically reducing the total computational power required compared to applying full processing across all pixels.
Data Source
AI summary
Disclosed herein are methods and systems for fast and edge preserving upsampling of a small data image based on one or more guide images. During the upsampling process, selected data from the one or more guide images are combined with data from the data image to generate an upsampled pixel in an upsampled image. The upsampling can occur directly from the data image or sequentially via one or more intermediate upsampled images.