Adaptive Image Contrast Processing via Edge-Based Kernel Selection
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Solution Overview
Problem
Current image contrast adjustment methods for electronic devices are limited to specific applications and fail to effectively process images with complex information, leading to issues like image fogging and ashing, as they use fixed parameters that are not adaptable to diverse image contents.
Innovation Solution
An adaptive image processing method that involves edge identification, determination of a filtering kernel based on edge information, separation of images into low-frequency and high-frequency components, enhancement processing of high-frequency images, and fusion with low-frequency images to produce a processed image, allowing for dynamic adjustment of filtering kernels and gain coefficients based on image content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If fixed parameters are used in image contrast processing, then the processing method is simple and easy to implement, but it causes image fogging and ashing problems and is not applicable to images with complex information
Solution Approach 1:
The patent transforms the static fixed-parameter contrast adjustment into a dynamic adaptive process. The filtering kernel parameters are dynamically adjusted based on local image characteristics such as edge information and variance. The system continuously adapts the filtering strength and type to match the local content, whether it be edge regions, flat regions, or textured regions, thereby resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The patent applies different filtering parameters to different regions of the image based on their local characteristics. Edge regions receive different treatment compared to flat regions or textured regions. The filtering kernel is locally adapted to preserve edges while enhancing contrast in appropriate regions, thus achieving both simplicity of implementation and adaptability to complex image contents.
2Manufacturing precision
If adaptive filtering processing is performed on the image, then image quality is improved and detail information is preserved, but the processing complexity increases
Solution Approach 1:
The patent segments the image processing task into distinct stages: edge detection, variance calculation, filtering kernel determination, and adaptive filtering application. Each stage handles a specific aspect of the processing, making the overall complex task more manageable and implementable. The segmentation allows the system to achieve high processing quality while keeping the implementation complexity controlled through modular design.
Solution Approach 2:
The patent changes the parameters of the filtering kernel adaptively based on image characteristics such as local variance and edge information. Instead of using fixed parameters, the system adjusts parameters like filtering strength, kernel size, and filter type according to the local image content. This parameter adaptation enables high processing quality while maintaining reasonable complexity through algorithmic flexibility.
Data Source
AI summary
A method for image processing, an electronic device, and a non-transitory storage medium are disclosed. The method includes obtaining an image captured by the camera and performing edge identification on the image; determining a filtering kernel for a filtering processing on the image according to a result of the edge identification; performing the filtering processing on the image based on the filtering kernel to obtain a low-frequency image and a high-frequency image corresponding to the image; and performing an enhancement processing for the high-frequency image and performing image fusion for the low-frequency image and the enhanced high-frequency image to obtain a processed image.