Enhanced retinex image processing via gradient domain guided image filtering and adaptive NLM
نویسندگان
1 College of Innovation and Entrepreneurship, Anhui Xinhua University, 230088, Hefei, China.
2 School of Graduate Studies, Management and Science University, Shah Alam 40100, Selangor, Malaysia.
doi
10.22105/riej.2025.545942.1685چکیده
To address the enhancement challenges in Low-Light (LOL ), non-uniform illumination, and underwater images,we propose an enhanced Retinex image processing method with good generalization capability. The research framework consists of the following key components: First, the raw RGB image is converted to Luminance-Inphase-Quadrature (YIQ) color space to separate luminance and chrominance components. Subsequently, the luminance component is enhanced using a Multi-Scale Retinex (MSR) algorithm based on Gradient Domain Guided Image Filtering (GDGIF), while adaptive Non-Local Means (NLM) filtering is introduced for noise suppression. For chrominance enhancement, a nonlinear gamma model is constructed to expand the display dynamic range. Finally, the processed YIQ image is reconstructed into RGB format. Experimental results on LOL, non-uniform illumination, and underwater image datasets demonstrate that the proposed method significantly outperforms traditional enhancement algorithms in terms of brightness adjustment, structure preservation, and detail clarity, providing an effective solution for image quality improvement in complex image enhancement domain.