Image Processing Method for Local Contrast Enhancement and Boundary Smoothing
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Solution Overview
Problem
Existing image processing techniques fail to effectively enhance contrast across different regions of an image, leading to obscured features and details due to mutual influences, and result in non-uniform areas between regions during local contrast processing.
Innovation Solution
A method and device that perform local contrast processing on each image region, analyzing local curves to adjust pixel values at boundaries between regions, using a weight scheme to smooth non-uniform areas and enhance display quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If contrast processing is performed on the whole image according to a fixed transfer function, then the gray-level range of the image is expanded, but the features or details in different regions are not obvious due to mutual influences
Solution Approach 1:
The image is divided into multiple regions, and contrast processing is performed separately on each region using region-specific transfer functions. This segmentation allows each region's features to be enhanced independently without mutual interference, resolving the contradiction between expanding gray-level range and maintaining feature distinguishability.
Solution Approach 2:
Different transfer functions are applied to different regions based on their local characteristics. Each region receives customized contrast enhancement tailored to its specific features, ensuring that local details are preserved while the overall gray-level range is expanded across the entire image.
2Manufacturing precision
If local contrast processing is performed on each image region, then the features or details in each region become obvious, but non-uniform areas appear in the boundary between regions
Solution Approach 1:
Boundary pixels are treated as intermediary elements between adjacent regions. The transfer function for boundary pixels is determined by combining the transfer functions of neighboring regions, creating a smooth transition zone that eliminates non-uniform areas while preserving the feature enhancement benefits of local contrast processing.
Solution Approach 2:
The transfer function applied to each pixel is dynamically determined based on its location. Boundary pixels dynamically combine information from multiple regions, while internal pixels use their region's dedicated transfer function. This dynamic approach ensures both local feature enhancement and boundary uniformity.
Data Source
AI summary
A method of image processing and a device thereof are provided in the present invention. First, an image having a plurality of regions is received, wherein each region comprises a plurality of pixels. A contrast process is performed on each region and a respective local curve of each region is analyzed, wherein the local curve is a corresponding relation between the gray-level values of the pixels in each region before and after the contrast process. Next, the gray-level value of one of the pixels in a boundary between one of the regions and the neighboring region is adjusted according to the respective local curves of the one of the regions and the neighboring region. The local contrast process towards to each region is performed for enhancing the image. Therefore, not only can the features be obvious, but the non-uniform area in the boundary between the regions also can be smoothed.