Image Resolution Enhancement via Pixel Value Transfer
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Low image resolution results in blurry pictures and videos, and existing methods struggle to effectively enhance spatial resolution for small details without distorting edges or causing ambiguity between lower and higher resolution versions.
Innovation Solution
A method that calculates a centralness metric with a negative slope to neighboring pixels, generates a delta signal using this metric and pixel values, and applies non-linear filtering to squeeze and boost pixel values, enhancing spatial resolution by producing a condensed and intensified detail.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing resolution enhancement methods are applied to enhance spatial resolution, then image clarity improves, but edge distortion and ambiguity between resolution versions occur
Solution Approach 1:
The patent applies different processing treatments to different regions of the image based on local characteristics. The centralness metric identifies center pixels of details, and these centers receive different treatment (value transfer and boosting) compared to non-center pixels. This local differentiation allows resolution enhancement while preserving edge integrity by not applying uniform enhancement across the entire image.
Solution Approach 2:
The patent changes the parameter of pixel values through the value transfer function and amplitude boosting. By transforming the pixel values using the centralness metric and applying non-linear filtering, the patent enhances resolution while controlling distortion through parameter transformation rather than simple interpolation.
2Measurement precision
If detail intensification is applied to enhance small details, then spatial resolution improves, but detail ambiguity between lower and higher resolution versions increases
Solution Approach 1:
The patent performs preliminary calculation of the centralness metric before applying value transfer. By pre-identifying the center pixels of details and calculating their centralness values, the patent establishes a foundation for subsequent value transfer that prevents ambiguity. This preliminary action ensures that when values are transferred and boosted, the origin and destination of each value change is clearly defined.
Solution Approach 2:
The centralness metric acts as an intermediary that mediates between the original pixel values and the enhanced values. It provides a continuous measure of how central a pixel is within a detail, allowing for smooth value transfer and boosting that maintains information continuity and reduces ambiguity between resolution versions.
3Measurement precision
If non-linear filtering is applied to squeeze and boost pixel values, then spatial resolution enhances, but processing complexity increases
Solution Approach 1:
The patent segments the image processing into distinct stages: calculating the centralness metric, determining which pixels are center pixels of details, applying value transfer to selected pixels, and performing amplitude boosting. This segmentation of the processing workflow makes the complex non-linear filtering more manageable and implementable while maintaining resolution enhancement effectiveness.
Data Source
AI summary
In one aspect, a method, includes producing a centralness metric that is highest at a center of detail from a set of pixels and having a negative slope to neighboring pixels from a center pixel at the center of detail, calculating a delta signal for the set of pixels using the produced centralness metric and pixel values of the set of pixels, generating a squeezed pixel value for each of the pixel values of the set of pixels, and outputting the generated squeezed pixel values.