Image Correction Method for Wide-Angle Camera Distortion
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Solution Overview
Problem
Current image correction methods in terminal devices with wide-angle cameras use a global optimization approach, leading to inefficient and ineffective correction processes as they distort objects that do not require correction, affecting processing efficiency and effect.
Innovation Solution
An image correction method that extracts human face attributes to identify regions for correction and protection, performing pixel compensation using background pixels to improve correction efficiency and ensure accurate processing effects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a global optimization approach is used for image correction, then the correction process can be simplified, but it distorts objects that do not require correction and reduces processing efficiency
Solution Approach 1:
The patent divides the image into multiple regions based on human face detection and attribute extraction. Different regions are identified as requiring correction, protection, or serving as background, allowing selective correction only where needed rather than applying global correction to the entire image.
Solution Approach 2:
The patent applies different correction strategies to different regions of the image. Human face regions are corrected using specific distortion parameters, while background regions are protected from correction. This local differentiation optimizes processing efficiency by avoiding unnecessary corrections in regions that do not require them.
2Device complexity
If a global optimization approach is used for image correction, then the correction process can be simplified, but it affects correction processing effect
Solution Approach 1:
The patent segments the image into correction regions, protection regions, and background regions based on human face detection. This segmentation enables precise correction only in areas where distortion needs to be corrected, while protecting other areas from unnecessary processing that would degrade quality.
Solution Approach 2:
Different correction parameters and processing methods are applied to different regions. Human face regions receive distortion correction, while background regions are excluded from correction processing. This local quality differentiation ensures high correction effectiveness where needed while maintaining overall image quality.
3Productivity
If human face attributes are extracted and regions are distinguished for correction and protection, then correction efficiency is improved, but the process complexity increases
Solution Approach 1:
The patent performs human face detection and attribute extraction before the correction process. By pre-identifying which regions require correction and which should be protected, the system can efficiently execute correction only on relevant regions, improving overall efficiency despite the additional preliminary processing steps.
Data Source
AI summary
An image correction method, a terminal device and a non-transitory computer readable storage medium are provided. The method includes: extracting human face attributes of an image; acquiring, from target regions, a first region having a human face correction attribute; acquiring, from the target regions, a second region having a human face protection attribute; and performing image correction on the human face in the first region, and performing pixel compensation, according to background pixels of the image, on a blank region generated by the image correction in the first region.