Image Correction Apparatus Using Adaptive Background Component Selection
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Solution Overview
Problem
Existing image correction methods are inefficient in reducing brightness unevenness in object images due to uneven background brightness, requiring manual adjustment by experienced operators and lacking automation.
Innovation Solution
An image correction method and apparatus that apply multiple blurring processes to object images to generate background component images with varying degrees of blurring, automatically selecting the most suitable image to correct brightness unevenness using index values, thereby reducing background brightness variations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual adjustment of correction degree is performed by operators, then image correction can be adapted to different objects and brightness unevenness levels, but the correction process becomes time-consuming and dependent on operator experience
Solution Approach 1:
The system automatically determines the correction degree by analyzing the object image itself, extracting background brightness information and calculating optimal correction parameters without requiring manual operator input. The correction apparatus self-adjusts based on the specific characteristics of each image, eliminating dependency on operator experience while maintaining accuracy.
Solution Approach 2:
The system dynamically adjusts correction parameters based on extracted background brightness distribution and calculated unevenness indices. By changing correction degree parameters automatically according to image-specific characteristics, the system achieves accurate correction for different objects and brightness conditions without manual intervention.
2Productivity
If simple blurring is applied to generate background images, then processing is fast and simple, but the background extraction accuracy is insufficient for complex brightness patterns
Solution Approach 1:
The background generation process is divided into multiple stages with different blurring degrees. Multiple background component images are generated with varying levels of blurring, and the most appropriate one is selected based on the object characteristics. This segmentation approach allows the system to balance processing efficiency with extraction accuracy by using lighter blurring when sufficient and stronger blurring only when necessary.
Solution Approach 2:
The blurring degree is dynamically adjusted based on the specific characteristics of the object image. The system evaluates brightness distribution patterns and automatically selects the appropriate blurring intensity from multiple pre-generated background components, making the background generation process adaptive rather than static.
3Measurement precision
If strong blurring is applied to extract background, then background brightness distribution is accurately captured, but object details are lost and correction quality decreases
Solution Approach 1:
Multiple background component images are generated with different blurring degrees, creating a spectrum from lightly to heavily blurred versions. The system segments the background extraction task into these multiple components and selects the most appropriate one based on object characteristics, avoiding the need to use strong blurring that would degrade image quality.
Solution Approach 2:
The system changes the blurring parameter dynamically based on the specific characteristics of each object image. By adjusting the blurring degree parameter to match the complexity of the brightness pattern, the system achieves accurate background extraction without applying excessive blurring that would harm object detail preservation.
Data Source
AI summary
An image correction method according to this invention includes an image acquisition step, a background generation step of generating a plurality of background component images corresponding to different degrees of blurring in the object image, a selection step of selecting one background component image based on index values obtained based on the plurality of background component images, and an image correction step of generating a corrected object image based on the object image and the one background component image.