Image Segmentation Correction for Low-Luminance AI Enhancement
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Solution Overview
Problem
Existing image processing techniques struggle to enhance visibility in low luminance environments effectively, particularly when using generative artificial intelligence, leading to inconsistencies in image brightness and color differences across divided images.
Innovation Solution
An image processing system that divides images into multiple segments, adjusts pixel values, reduces image size, determines corresponding regions, and corrects pixel values based on regional averages to ensure consistent luminance and color across combined images.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Illumination intensity
If generative artificial intelligence is used to change pixel values of low luminance images to high luminance images, then image visibility is improved, but inconsistencies in brightness and color differences occur across divided images
Solution Approach 1:
The image is divided into multiple divided images, and pixel values are changed for each divided image separately. This segmentation allows independent processing of each region while maintaining overall consistency through subsequent combination steps.
Solution Approach 2:
The system determines corresponding regions across divided images and uses pixel values from these regions to correct inconsistencies. This feedback mechanism ensures that brightness and color differences are minimized by referencing actual pixel data from corresponding areas.
2Manufacturing precision
If pixel values are changed for each divided image independently, then local image quality is improved, but brightness and color inconsistencies arise between divided images
Solution Approach 1:
The system determines corresponding regions across divided images and uses pixel values from these regions to correct inconsistencies. This feedback mechanism ensures that brightness and color differences are minimized by referencing actual pixel data from corresponding areas.
Solution Approach 2:
The system applies correction processing to ensure uniformity across divided images by using pixel values from corresponding regions. This homogenization process maintains consistent color and brightness characteristics across the entire image after combination.
3Productivity
If image processing is performed on divided images, then processing efficiency is improved, but complexity of the processing system increases
Solution Approach 1:
The image is divided into multiple divided images that can be processed independently and in parallel. This segmentation improves processing efficiency by allowing simultaneous operation on multiple regions while reducing the computational burden on individual processing units.
Solution Approach 2:
After independent processing of divided images, the system combines them by determining corresponding regions and correcting pixel values. This merging step integrates the separately processed images into a unified output, managing system complexity through structured combination procedures.
Data Source
AI summary
An image processing system includes processing circuitry. The processing circuitry divides an image to generate a plurality of divided images. The processing circuitry changes pixel values of each of the plurality of divided images. The processing circuitry reduces the image to generate a reduced image of the image. The processing circuitry changes pixel values of the reduced image. The processing circuitry determines a plurality of regions of the reduced image. Each region corresponds to each of the plurality of divided images having the changed pixel values. The processing circuitry corrects the changed pixel values of the divided image based on pixel values of one or more pixels included in the determined region of the reduced image. The processing circuitry combines a plurality of divided images each having the corrected pixel values.