A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines
Objective In coal mines, the uneven distribution of dust haze and complex illumination conditions caused by underground coal mining and dust removal lead to blurred video images, as well as the loss of information and details. Hence, this study proposed a dehazing and enhancement algorithm for heter...
Saved in:
| Main Authors: | , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | zho |
| Published: |
Editorial Office of Coal Geology & Exploration
2025-01-01
|
| Series: | Meitian dizhi yu kantan |
| Subjects: | |
| Online Access: | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.09.0602 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1823856584490483712 |
|---|---|
| author | Xuhui ZHANG Yanbin XIE Wenjuan YANG Chao ZHANG Jicheng WAN Zheng DONG Yanqun WANG Jie JIANG Long LI |
| author_facet | Xuhui ZHANG Yanbin XIE Wenjuan YANG Chao ZHANG Jicheng WAN Zheng DONG Yanqun WANG Jie JIANG Long LI |
| author_sort | Xuhui ZHANG |
| collection | DOAJ |
| description | Objective In coal mines, the uneven distribution of dust haze and complex illumination conditions caused by underground coal mining and dust removal lead to blurred video images, as well as the loss of information and details. Hence, this study proposed a dehazing and enhancement algorithm for heterogeneous images of underground mining environments. Methods Initially, hazy images were segmented into zones with different brightness values, for which the average ambient light intensity of global dark channels was calculated. The calculation results were integrated through weighting with the ambient light of local bright channels, which was obtained using adaptive gamma correction and multiscale Gaussian filtering. Consequently, accurate ambient light intensity estimates were determined. To preserve image details while achieving natural dehazing effects, transmission maps were processed using multiscale fusion correction technology and were then refined using joint bilateral filtering. Afterward, clear hazy images were obtained using the atmospheric scattering model. To further enhance the overall brightness and contrast of the dehazed images, white balance correction was performed. Specifically, images were converted into the hue-saturation-value (HSV) color space. Then, the details and contrast of images were enhanced using the proposed adaptive saturation correction and improved contrast enhancement algorithm, as well as Laplacian sharpening. Results and Conclusions Images of typical, actual scenarios were processed using five algorithms: dark channel prior (DCP), maximum reflectance prior (MRP), optimal-scale fusion-based dehazing (OSFD), multiscale fusion – low light image enhancement (MF-LIME), and contrast enhancement and exposure fusion (CEEF). The processing results of these algorithms were those of the proposed algorithm based on multiple indicators. The results indicate that compared to the above novel and excellent algorithms in terms of their optimal indicators, the proposed algorithm exhibited that: (1) The average gradients were approximately twice those obtained by CEEF, suggesting elevated image clarity. (2) The average information entropy decreased by approximately 1% compared to that of MRP, implying more information preserved. (3) The standard deviation increased by approximately 6% on average compared to OSFD, representing improved image contrast. (4) The average fog aware density evaluator (FADE) value by approximately 23% compared to CEEF, implying an effective reduction in the haze Therefore, the proposed algorithm can effectively improve the visual effects and quality of blurred images of underground mining environments in coal mines, exhibiting high utility in engineering. |
| format | Article |
| id | doaj-art-62898aa4cc1d4c44a90ed2eb340f6543 |
| institution | Kabale University |
| issn | 1001-1986 |
| language | zho |
| publishDate | 2025-01-01 |
| publisher | Editorial Office of Coal Geology & Exploration |
| record_format | Article |
| series | Meitian dizhi yu kantan |
| spelling | doaj-art-62898aa4cc1d4c44a90ed2eb340f65432025-02-12T07:20:18ZzhoEditorial Office of Coal Geology & ExplorationMeitian dizhi yu kantan1001-19862025-01-0153124525610.12363/issn.1001-1986.24.09.060224-09-0602zhang-xuhuiA dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal minesXuhui ZHANG0Yanbin XIE1Wenjuan YANG2Chao ZHANG3Jicheng WAN4Zheng DONG5Yanqun WANG6Jie JIANG7Long LI8College of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaCollege of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, ChinaObjective In coal mines, the uneven distribution of dust haze and complex illumination conditions caused by underground coal mining and dust removal lead to blurred video images, as well as the loss of information and details. Hence, this study proposed a dehazing and enhancement algorithm for heterogeneous images of underground mining environments. Methods Initially, hazy images were segmented into zones with different brightness values, for which the average ambient light intensity of global dark channels was calculated. The calculation results were integrated through weighting with the ambient light of local bright channels, which was obtained using adaptive gamma correction and multiscale Gaussian filtering. Consequently, accurate ambient light intensity estimates were determined. To preserve image details while achieving natural dehazing effects, transmission maps were processed using multiscale fusion correction technology and were then refined using joint bilateral filtering. Afterward, clear hazy images were obtained using the atmospheric scattering model. To further enhance the overall brightness and contrast of the dehazed images, white balance correction was performed. Specifically, images were converted into the hue-saturation-value (HSV) color space. Then, the details and contrast of images were enhanced using the proposed adaptive saturation correction and improved contrast enhancement algorithm, as well as Laplacian sharpening. Results and Conclusions Images of typical, actual scenarios were processed using five algorithms: dark channel prior (DCP), maximum reflectance prior (MRP), optimal-scale fusion-based dehazing (OSFD), multiscale fusion – low light image enhancement (MF-LIME), and contrast enhancement and exposure fusion (CEEF). The processing results of these algorithms were those of the proposed algorithm based on multiple indicators. The results indicate that compared to the above novel and excellent algorithms in terms of their optimal indicators, the proposed algorithm exhibited that: (1) The average gradients were approximately twice those obtained by CEEF, suggesting elevated image clarity. (2) The average information entropy decreased by approximately 1% compared to that of MRP, implying more information preserved. (3) The standard deviation increased by approximately 6% on average compared to OSFD, representing improved image contrast. (4) The average fog aware density evaluator (FADE) value by approximately 23% compared to CEEF, implying an effective reduction in the haze Therefore, the proposed algorithm can effectively improve the visual effects and quality of blurred images of underground mining environments in coal mines, exhibiting high utility in engineering.http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.09.0602regional segmentationdark-light channel fusioncontrast enhancementwhite balance correctionadaptive saturation correctionmining operations |
| spellingShingle | Xuhui ZHANG Yanbin XIE Wenjuan YANG Chao ZHANG Jicheng WAN Zheng DONG Yanqun WANG Jie JIANG Long LI A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines Meitian dizhi yu kantan regional segmentation dark-light channel fusion contrast enhancement white balance correction adaptive saturation correction mining operations |
| title | A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines |
| title_full | A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines |
| title_fullStr | A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines |
| title_full_unstemmed | A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines |
| title_short | A dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines |
| title_sort | dehazing and enhancement algorithm for heterogeneous images of underground mining environments in coal mines |
| topic | regional segmentation dark-light channel fusion contrast enhancement white balance correction adaptive saturation correction mining operations |
| url | http://www.mtdzykt.com/article/doi/10.12363/issn.1001-1986.24.09.0602 |
| work_keys_str_mv | AT xuhuizhang adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT yanbinxie adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT wenjuanyang adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT chaozhang adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT jichengwan adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT zhengdong adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT yanqunwang adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT jiejiang adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT longli adehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT xuhuizhang dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT yanbinxie dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT wenjuanyang dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT chaozhang dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT jichengwan dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT zhengdong dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT yanqunwang dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT jiejiang dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines AT longli dehazingandenhancementalgorithmforheterogeneousimagesofundergroundminingenvironmentsincoalmines |