An Optimized LIME Scheme for Medical Low Light Level Image Enhancement

Area of Science:

• Medical Imaging
• Computer Vision
• Image Processing

Background:

• Medical image technology is crucial for accurate diagnosis and treatment.
• Low-light conditions often hinder the clarity and interpretability of medical images.
• Enhancing medical images is vital for improving diagnostic accuracy.

Purpose of the Study:

• To develop a simple and effective low-light image enhancement method inspired by the human vision system (HVS).
• To improve the quality of medical images for better analysis and diagnosis.
• To reduce visual information distortions in enhanced images.

Main Methods:

• Utilizing a sampler to determine the optimal exposure ratio for a camera response model.
• Employing a generator to synthesize dual-exposure images, correcting underexposed regions.
• Integrating illumination map estimation (LIME) and a combiner for image fusion.
• Applying postprocessing with block machine 3-dimensional (BM3D) for denoising and recomposition.

Main Results:

• The proposed method effectively enhances low-light images with minimal visual information distortion.
• Synthesized dual-exposure images improve exposure in previously underexposed areas.
• Postprocessing refines image quality through gray range adjustment, denoising, and recomposition.

Conclusions:

• The developed low-light image enhancement technique offers significant improvements for medical imaging applications.
• Enhanced medical images facilitate more accurate grasping of details and characteristics by medical professionals.
• This method aids medical workers in more convenient image analysis and judgment, potentially improving patient care.