Image Boundary Sharpness via Gradient Displacement Vectors
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Solution Overview
Problem
Digital image zooming processes, such as pixel interpolation, blur object boundaries due to averaging color values, leading to reduced image quality and impaired editing precision.
Innovation Solution
A system computes displacement vectors proportional to color value gradients, replacing pixel values with values from the end of these vectors to enhance boundary sharpness, using techniques like Sobel operators and Laplacian of Gaussian filters, and normalizing vector magnitudes to prevent outliers.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If pixel interpolation is used during digital zoom operation, then new pixels are added between existing pixels, but object boundaries become blurred due to averaging color values
Solution Approach 1:
The patent applies sharpening operations before the final interpolation step. By pre-processing the image to enhance edge contrast and then performing interpolation, the boundary sharpness is preserved better than if interpolation were done first. This preliminary action prevents the blurring effect from dominating the final result.
Solution Approach 2:
The patent modifies color values based on gradient magnitude and direction, applying different transformation parameters to different regions of the image. Edges with high gradient magnitudes receive stronger sharpening effects, while smooth regions maintain their original characteristics. This parameter-based differentiation resolves the contradiction by applying sharpening selectively where boundaries exist.
2Ease of operation
If pixel interpolation averages color values of surrounding pixels, then new pixels are generated, but high-contrast transitions on object boundaries are rendered into gradual transitions with lower contrast
Solution Approach 1:
The patent computes gradient magnitudes and directions for each pixel and applies localized color value adjustments based on these local characteristics. Pixels near object boundaries with high gradient magnitudes receive different treatment compared to pixels in smooth regions. This local quality approach preserves color contrast precision at boundaries while maintaining the ability to generate new pixels through interpolation.
Solution Approach 2:
Instead of accepting the natural blurring effect of interpolation, the patent inverts the approach by applying sharpening operations that counteract the blurring. The gradient-based displacement moves pixel values in the opposite direction of the blurring effect, restoring high-contrast transitions that would otherwise be lost during interpolation.
3Manufacturing precision
If gradient-based displacement is applied to sharpen boundaries, then color contrast across object boundaries increases, but computational complexity increases due to gradient computation and vector normalization
Solution Approach 1:
The patent applies gradient computation and displacement primarily to regions where boundaries are likely to exist, rather than uniformly processing the entire image. By focusing computational resources on edge regions identified through gradient analysis, the patent achieves boundary sharpening with reduced overall computational complexity compared to full-image processing.
Data Source
AI summary
One embodiment of the present invention provides a system that enhances sharpness of object boundaries in an image. During operation, the system first receives an image. Next, the system computes gradients of color values for pixels in the image. The system then computes displacement vectors for pixels in the image, wherein the magnitude of the displacement vector for a given pixel is proportional to the magnitude of the gradient of the color value at the given pixel. The system next replaces the color values for the pixels, wherein the color value for a given pixel is replaced with a color value obtained from a location at the end of the displacement vector for the given pixel.