Image contrast enhancement and brightness preservation based on an adaptive histogram correction framework

Area of Science:

• Computer Vision
• Image Processing

Background:

• Image enhancement is crucial for advanced computer vision tasks.
• Traditional histogram modification and gamma correction methods often require manual parameter tuning, impacting results.
• Parameter dependency limits the effectiveness and adaptability of existing enhancement techniques.

Purpose of the Study:

• To propose a new histogram modification-based method for image contrast enhancement.
• To achieve global luminance preservation alongside enhanced contrast.
• To overcome the limitations of parameter-dependent traditional methods.

Main Methods:

• Introduced an adaptive histogram modification framework.
• Combined adaptive histogram modification with adaptive gamma correction.
• Developed a method that preserves original histogram features for adaptive gamma correction.

Main Results:

• The proposed method enhances image contrast effectively.
• Global luminance is preserved, and artifacts are suppressed.
• Experimental results demonstrate competitive or superior performance compared to popular methods.

Conclusions:

• The novel adaptive histogram modification method offers robust image enhancement.
• The approach successfully balances contrast enhancement with luminance preservation.
• This technique provides a parameter-free, effective solution for image enhancement challenges.