A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image Enhancement

Sand-dust images often exhibit poor visibility, color distortion, and low contrast due to atmospheric scattering. This paper presents a novel intensity-corrected blue channel compensation and edge-preserving contrast enhancement technique using Laplacian filtering and sigmoid function-based saturation adjustment. The proposed method first corrects the blue channel intensity and applies white balancing to mitigate color cast. The preprocessed image is converted to HSV color space, where the V-channel undergoes contrast-limited adaptive histogram equalization (CLAHE) followed by Gaussian smoothing and Laplacian filtering for edge-preserving contrast enhancement. Simultaneously, the S-channel is refined using a sigmoid function to enhance color saturation adaptively. The enhanced HSV image is reconstructed into RGB space to obtain the final output. The method is quantitatively evaluated using image quality metrics including UIQM, UICM, UISM, SSIM, PSNR, and CIE94, and its computational efficiency is assessed via an Energy Efficiency Index (EEI). Experimental results demonstrate significant improvements in visibility, contrast, and color fidelity of sand-dust images while maintaining low computational cost, making the approach suitable for real-time applications in remote sensing and outdoor vision systems.

Keywords: Sand-Dust Image Enhancement, Blue Channel Compensation, Laplacian Filter, Sigmoid Function, Contrast Enhancement, Image Quality Metrics, Energy Efficiency.