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

Outdoor computer vision systems face significant challenges due to reduced visibility and severe color distortion in the images captured in sand-dust-affected environments. This study aims to improve the visibility of sand-dust-degraded images. To achieve this goal, a novel and effective method is proposed to remove the sand-dust color cast and enhance image visibility. The proposed method combines two essential color model methods to remove the sand-dust color cast and enhance image clarity. In the initial phase, sand-dust removal is achieved using a novel Intensity-corrected blue channel compensation along with white balancing for color adjustment based on the Red-Green-Blue (RGB) color model. In the next phase, a novel Edge-preserving contrast enhancement method is applied to improve the visibility under sand-dust conditions. This method consists of CLAHE, a Gaussian blur filter, a Laplace filter, and the sigmoid function. Using the Hue-Saturation-Value (HSV) color model, CLAHE is applied for contrast enhancement; the Gaussian blur filter removes high-frequency noise, and the Laplace filter enhances edge detection, all targeting the V (Value) channel to refine image details, while the sigmoid function adjusts saturation in the Saturation (S) channel, ensuring natural color balance and improved feature visibility. In-depth qualitative and quantitative evaluations are conducted on images with varying levels of sand-dust intensity (weak, moderate, strong, extreme). The proposed method shows superior performance in Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and processing speed, while significantly reducing computational complexity. Compared to the state-of-the-art CNN and all previous methods, our proposed method is efficient for real-time applications with minimal hardware requirements, making it ideal for embedded vision systems. Furthermore, a novel Energy Efficiency Index (EEI) is used to assess computational cost-effectiveness. The evaluation results confirm that the proposed method outperforms all previous and advanced deep learning methods in terms of visual quality, metrics, time complexity, and energy efficiency, making it a promising solution for sand-dust image enhancement in real-world applications.