Chromatic Aberration Correction Using Gradient Weights
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Solution Overview
Problem
Existing image processing methods are inefficient in correcting chromatic aberration, particularly in edge regions and due to blooming, which results in significant luminance differences and strong chrominance, and are costly to implement in non-high-performance cameras.
Innovation Solution
A chromatic aberration correction apparatus and method that senses chromatic aberration regions, calculates weights based on color and luminance gradients, and corrects chrominance values using these weights, incorporating a Sobel filter for edge detection and chrominance modeling to address various types of chromatic aberration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If conventional image processing methods are used to correct chromatic aberration, then chromatic aberration in edge regions and blooming effects are reduced, but the correction effectiveness is insufficient for strong chrominance regions
Solution Approach 1:
The patent applies different processing strategies to different regions of the image based on their chrominance characteristics. For regions with strong chrominance (blooming areas), a specialized correction method using chromatic aberration characteristic curves is applied, while other regions use conventional correction methods. This localized approach allows the system to effectively handle both subtle and severe chromatic aberration cases.
Solution Approach 2:
The patent changes the correction parameters dynamically based on the chrominance strength of each region. By detecting the chrominance magnitude and selecting different correction curves accordingly, the system adapts its correction strength and method to match the local image characteristics, thereby improving correction effectiveness across diverse regions.
2Manufacturing precision
If special glass material or specially processed lens is used to reduce chromatic aberration, then chromatic aberration is reduced, but the manufacturing cost increases
Solution Approach 1:
The patent replaces the mechanical/optical solution (special glass material or specially processed lens) with an image processing solution. By using digital image processing algorithms to detect and correct chromatic aberration in the captured image, the system achieves effective chromatic aberration reduction without requiring expensive specialized lenses, thereby reducing manufacturing costs while maintaining correction effectiveness.
3Manufacturing precision
If image restoration process is applied to enhance edge regions, then image quality is improved, but chromatic aberration in edge regions becomes more noticeable
Solution Approach 1:
The patent converts the harmful effect of enhanced chromatic aberration visibility in edge regions into a benefit by using the enhanced edges as clear indicators for chromatic aberration detection. The image restoration process, which initially worsens chromatic aberration visibility, actually helps the system identify edge regions more accurately, allowing the correction algorithm to target these regions more effectively and remove the false colors.
Data Source
AI summary
Provided are a method and apparatus for correcting chromatic aberration of an image, the method and apparatus used in an image processing apparatus for an image pickup device or a display image processing apparatus. The chromatic aberration correction apparatus includes a chromatic aberration region sensing unit analyzing a luminance signal of an input image and sensing a region having chromatic aberration; a color gradient calculation unit calculating a first weight, which indicates a degree of chromatic aberration, based on a difference between gradients of color components of the input image; a luminance gradient calculation unit calculating a second weight, which indicates the degree of chromatic aberration, based on gradients of luminance components of the input image; and a chrominance correction unit correcting chrominance of a pixel of the input image, which is included in the sensed region, based on a value obtained by multiplying the first weight by the second weight.


