Gradient-Based Image Interpolation for Edge Preservation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional image interpolation methods, such as linear interpolation, suffer from blurring and artifacts around edges and lines due to their low-pass filter properties, which cannot maintain low computation cost while eliminating these issues.
Innovation Solution
An image interpolation method and device that adjust the interpolation process based on gradient direction, assigning larger weights to pixels along the gradient direction and smaller weights to those orthogonal to it, using a gradient-based interpolation filter to preserve high-frequency details and prevent edge blurring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If linear interpolation method is used, then computation cost is reduced, but image quality deteriorates with blurring and artifacts around edges
Solution Approach 1:
The patent applies different interpolation strategies to different regions of the image based on gradient magnitude. For high-gradient regions (edges), it uses a smaller interpolation kernel with adjusted weights to preserve edge sharpness, while for low-gradient regions it uses standard linear interpolation. This local adaptation resolves the contradiction by maintaining low computation cost overall while improving quality specifically where needed.
Solution Approach 2:
The patent dynamically adjusts the interpolation kernel size and weight distribution based on the local gradient magnitude at each pixel position. By making the interpolation parameters adaptive rather than fixed, the system can optimize between computation cost and image quality for each local region, preventing edge blurring while maintaining efficiency in smooth areas.
2Speed
If conventional linear interpolation is used, then processing speed is improved, but edge sharpness deteriorates due to low-pass filter properties
Solution Approach 1:
The patent applies gradient-based weight adjustment locally at edge regions while maintaining standard linear interpolation in non-edge regions. This selective application preserves processing speed for the majority of pixels while improving edge sharpness only where gradient magnitude indicates edge presence, resolving the speed-quality tradeoff.
Solution Approach 2:
The patent segments the image processing into two distinct paths: standard linear interpolation for most pixels and gradient-adjusted interpolation for edge pixels. By segmenting the processing based on gradient magnitude thresholds, the system maintains high processing speed while applying edge-preserving enhancement only where necessary.
3Manufacturing precision
If gradient-based weighting is applied, then image quality is improved, but device complexity increases
Solution Approach 1:
The patent applies gradient-based weighting selectively only to pixels identified as edge pixels through gradient magnitude thresholding, rather than to all pixels. This partial application reduces the overall computational burden and algorithmic complexity compared to applying gradient analysis universally, while still achieving quality improvement where it matters most.
Data Source
AI summary
Image interpolation methods for interpolating a destination pixel of a destination image by using a plurality of source pixels of a source image are provided. The method includes the steps of: selecting the plurality of source pixels from the source image corresponding to the destination pixel; obtaining a gradient direction and a gradient magnitude according to the plurality o source pixels; determining a weight corresponding to each of the plurality of source pixels according to the gradient direction and the gradient magnitude; and performing an interpolation operation according to pixel values of the plurality of source pixels and the weight corresponding to each of the plurality of source pixels to obtain a pixel value of the destination pixel.