Adaptive contrast enhancement and effect of non-linear exponential merit function on computer-generated holograms

Area of Science:

• Optics and Photonics
• Computational Imaging

Background:

• Gerchberg-Saxton algorithm is a common method for computer-generated holography.
• Existing methods often struggle with limited contrast and dynamic range in reconstructed images.

Purpose of the Study:

• To introduce a novel merit function for computer-generated holograms.
• To enhance contrast and dynamic range of reconstructed holographic images.

Main Methods:

• Integrated energy redistribution into the Gerchberg-Saxton method.
• Employed a non-linear exponent for intensity weighting.
• Utilized a dynamic range shift to suppress noise in visually sensitive regions.

Main Results:

• Achieved superior contrast ratios and maintained overall luminance.
• Produced sharper and more vivid reconstructed holographic images.
• Demonstrated a 6.25% improvement in peak signal-to-noise ratio and a 67.85% improvement in dynamic range index.

Conclusions:

• The proposed non-linear merit function effectively enhances holographic image quality.
• The method offers significant improvements in contrast, dynamic range, and image clarity.
• Results show practical benefits for computer-generated holography applications.