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 p...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10915637/ |
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| author | Muhammad Khawaja Kashif Masood Enrique Nava Baro Pablo Otero Roth |
| author_facet | Muhammad Khawaja Kashif Masood Enrique Nava Baro Pablo Otero Roth |
| author_sort | Muhammad Khawaja Kashif Masood |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3bc4a5cae7d4437db43ab58abdb8c878 |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3bc4a5cae7d4437db43ab58abdb8c8782025-08-20T02:55:53ZengIEEEIEEE Access2169-35362025-01-0113431274314410.1109/ACCESS.2025.354897210915637A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image EnhancementMuhammad Khawaja Kashif Masood0https://orcid.org/0009-0009-0234-9402Enrique Nava Baro1https://orcid.org/0000-0001-7817-6442Pablo Otero Roth2https://orcid.org/0000-0003-3042-4392Institute of Oceanic Engineering Research, University of Málaga, Malaga, SpainInstitute of Oceanic Engineering Research, University of Málaga, Malaga, SpainInstitute of Oceanic Engineering Research, University of Málaga, Malaga, SpainOutdoor 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.https://ieeexplore.ieee.org/document/10915637/Sand-dust imagesintensity corrected blue channel compensationcontrast enhancementLaplace filtersigmoid functionCLAHE |
| spellingShingle | Muhammad Khawaja Kashif Masood Enrique Nava Baro Pablo Otero Roth A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image Enhancement IEEE Access Sand-dust images intensity corrected blue channel compensation contrast enhancement Laplace filter sigmoid function CLAHE |
| title | A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image Enhancement |
| title_full | A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image Enhancement |
| title_fullStr | A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image Enhancement |
| title_full_unstemmed | A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image Enhancement |
| title_short | A Novel Intensity-Corrected Blue Channel Compensation and Edge-Preserving Contrast Enhancement Using Laplace Filter and Sigmoid Function for Sand-Dust Image Enhancement |
| title_sort | novel intensity corrected blue channel compensation and edge preserving contrast enhancement using laplace filter and sigmoid function for sand dust image enhancement |
| topic | Sand-dust images intensity corrected blue channel compensation contrast enhancement Laplace filter sigmoid function CLAHE |
| url | https://ieeexplore.ieee.org/document/10915637/ |
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