Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator
Extreme weather conditions like fog and haze present substantial challenges to object recognition systems. Reduced visibility and contrast degradation significantly affect the auto-correlation process, often leading to failure in object recognition. To address this critical issue and to make object...
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MDPI AG
2024-12-01
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| Series: | Photonics |
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| Online Access: | https://www.mdpi.com/2304-6732/11/12/1142 |
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| author | Jyoti Bikash Mohapatra Naveen K. Nishchal Jyothish Monikantan |
| author_facet | Jyoti Bikash Mohapatra Naveen K. Nishchal Jyothish Monikantan |
| author_sort | Jyoti Bikash Mohapatra |
| collection | DOAJ |
| description | Extreme weather conditions like fog and haze present substantial challenges to object recognition systems. Reduced visibility and contrast degradation significantly affect the auto-correlation process, often leading to failure in object recognition. To address this critical issue and to make object recognition accurate and invincible, we propose a hybrid digital–optical correlator specifically designed to perform under adverse weather conditions. This approach integrates the dark channel prior (DCP) with the fringe-adjusted joint transform correlator (FJTC), promising significant potential to enhance the robustness of the object recognition process under challenging environmental conditions. The proposed scheme presents a unique and alternative approach for object recognition under bad weather conditions. The incoming input scenes are processed with the DCP, enabling the FJTC to perform optical correlation on the refined images. The effectiveness of the proposed method is evaluated using several performance metrics like the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), correlation peak intensity (CPI), processing time, and recognition accuracy. To validate the performance of the proposed study, numerical simulation along with hybrid digital–optical demonstrations have been conducted. |
| format | Article |
| id | doaj-art-0ba7691e822040c5848e8241c16f5cfb |
| institution | Kabale University |
| issn | 2304-6732 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Photonics |
| spelling | doaj-art-0ba7691e822040c5848e8241c16f5cfb2024-12-27T14:47:13ZengMDPI AGPhotonics2304-67322024-12-011112114210.3390/photonics11121142Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform CorrelatorJyoti Bikash Mohapatra0Naveen K. Nishchal1Jyothish Monikantan2Department of Physics, Indian Institute of Technology Patna, Bihta, Patna 801106, IndiaDepartment of Physics, Indian Institute of Technology Patna, Bihta, Patna 801106, IndiaISRO Inertial Systems Unit, Thiruvananthapuram 695013, IndiaExtreme weather conditions like fog and haze present substantial challenges to object recognition systems. Reduced visibility and contrast degradation significantly affect the auto-correlation process, often leading to failure in object recognition. To address this critical issue and to make object recognition accurate and invincible, we propose a hybrid digital–optical correlator specifically designed to perform under adverse weather conditions. This approach integrates the dark channel prior (DCP) with the fringe-adjusted joint transform correlator (FJTC), promising significant potential to enhance the robustness of the object recognition process under challenging environmental conditions. The proposed scheme presents a unique and alternative approach for object recognition under bad weather conditions. The incoming input scenes are processed with the DCP, enabling the FJTC to perform optical correlation on the refined images. The effectiveness of the proposed method is evaluated using several performance metrics like the structural similarity index measure (SSIM), peak signal-to-noise ratio (PSNR), correlation peak intensity (CPI), processing time, and recognition accuracy. To validate the performance of the proposed study, numerical simulation along with hybrid digital–optical demonstrations have been conducted.https://www.mdpi.com/2304-6732/11/12/1142joint transform correlatorfringe-adjusted filterhazefogdark channel prior |
| spellingShingle | Jyoti Bikash Mohapatra Naveen K. Nishchal Jyothish Monikantan Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator Photonics joint transform correlator fringe-adjusted filter haze fog dark channel prior |
| title | Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator |
| title_full | Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator |
| title_fullStr | Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator |
| title_full_unstemmed | Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator |
| title_short | Object Recognition in Foggy and Hazy Conditions Using Dark Channel Prior-Based Fringe-Adjusted Joint Transform Correlator |
| title_sort | object recognition in foggy and hazy conditions using dark channel prior based fringe adjusted joint transform correlator |
| topic | joint transform correlator fringe-adjusted filter haze fog dark channel prior |
| url | https://www.mdpi.com/2304-6732/11/12/1142 |
| work_keys_str_mv | AT jyotibikashmohapatra objectrecognitioninfoggyandhazyconditionsusingdarkchannelpriorbasedfringeadjustedjointtransformcorrelator AT naveenknishchal objectrecognitioninfoggyandhazyconditionsusingdarkchannelpriorbasedfringeadjustedjointtransformcorrelator AT jyothishmonikantan objectrecognitioninfoggyandhazyconditionsusingdarkchannelpriorbasedfringeadjustedjointtransformcorrelator |