Image Display Luminance Correction for Haze-Affected Capture
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Solution Overview
Problem
Electronic devices often capture images with distorted quality due to factors such as sensor, display, or lens issues, leading to haze phenomena that affect luminance and contrast, making it difficult to represent dark regions properly and causing overall brightness distortion.
Innovation Solution
The electronic device employs a processor to detect haze levels in images by analyzing luminance values and applies luminance shift and contrast stretching techniques to convert and display images with improved luminance and contrast, ensuring clear representation of both bright and dark regions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If the camera captures images directly through the sensor and lens, then the image acquisition process is simple and fast, but the image quality is distorted due to haze phenomena and luminance inaccuracies
Solution Approach 1:
The system performs preliminary haze detection and luminance correction by analyzing the captured image's luminance values, identifying haze levels, and applying appropriate correction algorithms before final image display. This preliminary processing step resolves the contradiction by preparing the image data in advance to achieve high quality output without requiring complex hardware modifications.
Solution Approach 2:
The system changes the luminance parameters of the image by detecting haze levels and applying luminance shift and contrast stretching transformations. This parameter-based approach allows the system to correct image quality issues through software processing rather than requiring complex optical hardware, thus resolving the contradiction between image quality and device complexity.
2Manufacturing precision
If the image is displayed with original luminance values, then the processing time is short, but the dark regions are not properly represented and overall brightness is distorted
Solution Approach 1:
The system performs self-service by automatically detecting haze levels in the captured image and applying appropriate luminance correction without requiring manual intervention. The processor autonomously analyzes luminance values, determines haze conditions, and executes correction algorithms, thereby achieving accurate luminance representation while minimizing processing time through automated decision-making.
Solution Approach 2:
The system dynamically changes luminance parameters based on detected haze levels, applying luminance shift and contrast stretching only when haze is detected. This conditional parameter adjustment approach ensures accurate luminance representation while avoiding unnecessary processing operations, thus resolving the contradiction between luminance accuracy and processing time.
3Manufacturing precision
If contrast stretching is applied to enhance dark regions, then the visibility of dark areas is improved, but the overall image may become overly bright or lose natural luminance relationships
Solution Approach 1:
The system applies local quality enhancement by performing contrast stretching selectively on dark regions of the image rather than uniformly across the entire image. By identifying areas with luminance values below a certain threshold and applying enhancement only to those regions, the system improves dark region visibility while preserving the natural luminance relationships in brighter areas, thus resolving the contradiction between dark region representation and overall luminance accuracy.
Solution Approach 2:
The system applies partial action by performing contrast stretching only to the extent necessary to improve dark region visibility, using controlled scaling factors that prevent over-enhancement. This partial application of contrast stretching ensures that dark regions become visible while maintaining natural luminance relationships in the overall image, resolving the contradiction between enhancing dark areas and preserving overall luminance accuracy.
Data Source
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AI summary
In embodiments, an electronic device is provided. The electronic device may comprise a camera, a display and at least one processor. The at least one processor may be configured to: acquire, through the camera, a first image having first luminance values; acquire a haze level according to a specified sub-ratio of the first luminance values of the first image; and, on the basis of identifying that the haze level is greater than or equal to a threshold, display, through the display, a second image having final luminance values. The final luminance values can be acquired on the basis of: a luminance shift for converting the first luminance values according to a difference between a central luminance level of the first luminance values and a target luminance level; and contrast stretching for scaling an interval between luminance levels from the target luminance level.