GLAGC: Adaptive Dual-Gamma Function for Image Illumination Perception and Correction in the Wavelet Domain

Area of Science:

• Computer Vision
• Image Processing
• Signal Processing

Background:

• Real-world images often suffer from low contrast and uneven illumination, hindering detail visibility and pattern recognition.
• Existing methods may struggle with complex lighting variations, necessitating advanced adaptive correction techniques.

Purpose of the Study:

• To propose an automatic image illumination perception and adaptive correction algorithm (GLAGC) for enhancing image quality under adverse lighting.
• To improve detail preservation and pattern recognition capabilities in images with low or uneven illumination.

Main Methods:

• Utilizing Retinex theory and discrete wavelet transform to extract image illumination.
• Designing spatial luminance distribution and global statistical luminance features for adaptive gamma correction.
• Employing maximum entropy criterion for estimating illumination-feature relationships and applying global/local gamma correction.
• Incorporating smoothness preservation in high-frequency subbands and deriving an adaptive stabilization factor to mitigate noise.

Main Results:

• The proposed GLAGC algorithm effectively corrects images with low-contrast and uneven illumination.
• Experimental results show superior or comparable performance against state-of-the-art methods in terms of image quality and processing efficiency.
• The algorithm successfully preserves edge smoothness and reduces noise after wavelet reconstruction.

Conclusions:

• GLAGC provides an effective solution for enhancing image perception and pattern recognition in challenging real-world lighting conditions.
• The method demonstrates robustness and efficiency, offering a valuable tool for various image processing applications.
• The proposed features and adaptive correction strategies contribute to improved image detail and clarity.