Enhancing Image Quality using Histogram Equalization Techniques for Improved Visual Analysis
Keywords:
Bi-histogram equalization, Brightness preserving bi-histogram equalization, Contrast enhancement, Histogram equalization, Image enhancement, Quantized histogramAbstract
Enhancing image quality plays a critical role in applications requiring accurate visual analysis, particularly in healthcare imaging and adverse weather conditions such as foggy environments. This work explores the application of advanced histogram-based image enhancement techniques, including Histogram Equalization (HE), Bi Histogram Equalization (BHE) Quantized Histogram methods, and Brightness-preserving approaches. In healthcare, improved contrast in medical images, such as X-rays and MRI scans, aids in accurate diagnosis and better visualization of anomalies. By employing HE and BHE, the contrast is enhanced while preserving critical brightness levels, ensuring diagnostic integrity. Similarly, in foggy day scenarios, these methods improve visibility in outdoor images, facilitating clearer scene recognition for navigation, surveillance, and autonomous vehicle systems. Quantized histogram techniques are applied to reduce computational complexity, making real-time processing feasible without significant loss of details. Brightness-preserving methods, such as brightness-preserving histogram Equalization (BBHE), are utilized to ensure that the enhanced images retain natural illumination, avoiding over-enhancement that could hinder practical usability. This work demonstrates the effectiveness of these methods through case studies in healthcare imaging and outdoor scenarios, showcasing their potential to improve safety, diagnostics, and environmental monitoring. The results underscore the value of tailored histogram-based enhancement techniques in addressing diverse application needs while maintaining visual fidelity.
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