MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN
<p dir="ltr">Various vision-based tasks suffer from inaccurate navigation and poor performance due to inevitable problems, such as adverse weather conditions like haze, fog, rain, snow, and clouds affecting ground and aerial navigation, as well as underwater images being degraded wit...
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2023
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| _version_ | 1864513527499194368 |
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| author | Maria Siddiqua (17949149) |
| author2 | Samir Brahim Belhaouari (16855434) Naeem Akhter (17949152) Aneela Zameer (17095214) Javaid Khurshid (17949155) |
| author2_role | author author author author |
| author_facet | Maria Siddiqua (17949149) Samir Brahim Belhaouari (16855434) Naeem Akhter (17949152) Aneela Zameer (17095214) Javaid Khurshid (17949155) |
| author_role | author |
| dc.creator.none.fl_str_mv | Maria Siddiqua (17949149) Samir Brahim Belhaouari (16855434) Naeem Akhter (17949152) Aneela Zameer (17095214) Javaid Khurshid (17949155) |
| dc.date.none.fl_str_mv | 2023-06-26T06:00:00Z |
| dc.identifier.none.fl_str_mv | 10.1109/access.2023.3289591 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/MACGAN_An_All-in-One_Image_Restoration_Under_Adverse_Conditions_Using_Multidomain_Attention-Based_Conditional_GAN/25205192 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Snow Feature extraction Rain Image restoration Image color analysis Degradation Clouds Weather forecasting Generative adversarial networks Restoration multidomain adverse weather navigation aerial marine attention mechanism |
| dc.title.none.fl_str_mv | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <p dir="ltr">Various vision-based tasks suffer from inaccurate navigation and poor performance due to inevitable problems, such as adverse weather conditions like haze, fog, rain, snow, and clouds affecting ground and aerial navigation, as well as underwater images being degraded with blue-green tones and mud affecting marine navigation. Existing techniques in the literature typically focus on restoring specific degradations using separate models, leading to computational inefficiency. To address this, an all-in-one Multidomain Attention-based Conditional Generative Adversarial Network (MACGAN) is proposed to improve scene visibility for optimal ground, aerial, and marine navigation, using the same set of parameters across all domains. The MACGAN is a lightweight network with four encoder and decoder blocks and multiple attention blocks in between, which enhances the image restoration process by focusing on the most important features. To evaluate the effectiveness of MACGAN, extensive qualitative and quantitative comparisons are conducted with state-of-the-art image-to-image translation models, all-in-one adverse weather removal models, and single-effect removal models. The results highlight the superior performance of MACGAN in terms of scene visibility improvement and restoration quality. Additionally, MACGAN is tested on real-world unseen image domains, including smog, dust, fog, rain, snow, and lightning, further validating its generalizability and robustness. Furthermore, an ablation study is conducted to analyze the contributions of the discriminator and attention blocks within the MACGAN architecture. The results confirm that both components play significant roles in the effectiveness of MACGAN, with the discriminator ensuring adversarial training and the attention blocks effectively capturing and enhancing important image features.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3289591" target="_blank">https://dx.doi.org/10.1109/access.2023.3289591</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_2a26f2224356db678cbfd21e6e5c4ef3 |
| identifier_str_mv | 10.1109/access.2023.3289591 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/25205192 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GANMaria Siddiqua (17949149)Samir Brahim Belhaouari (16855434)Naeem Akhter (17949152)Aneela Zameer (17095214)Javaid Khurshid (17949155)EngineeringElectrical engineeringElectronics, sensors and digital hardwareMaterials engineeringSnowFeature extractionRainImage restorationImage color analysisDegradationCloudsWeather forecastingGenerative adversarial networksRestorationmultidomainadverse weathernavigationaerialmarineattention mechanism<p dir="ltr">Various vision-based tasks suffer from inaccurate navigation and poor performance due to inevitable problems, such as adverse weather conditions like haze, fog, rain, snow, and clouds affecting ground and aerial navigation, as well as underwater images being degraded with blue-green tones and mud affecting marine navigation. Existing techniques in the literature typically focus on restoring specific degradations using separate models, leading to computational inefficiency. To address this, an all-in-one Multidomain Attention-based Conditional Generative Adversarial Network (MACGAN) is proposed to improve scene visibility for optimal ground, aerial, and marine navigation, using the same set of parameters across all domains. The MACGAN is a lightweight network with four encoder and decoder blocks and multiple attention blocks in between, which enhances the image restoration process by focusing on the most important features. To evaluate the effectiveness of MACGAN, extensive qualitative and quantitative comparisons are conducted with state-of-the-art image-to-image translation models, all-in-one adverse weather removal models, and single-effect removal models. The results highlight the superior performance of MACGAN in terms of scene visibility improvement and restoration quality. Additionally, MACGAN is tested on real-world unseen image domains, including smog, dust, fog, rain, snow, and lightning, further validating its generalizability and robustness. Furthermore, an ablation study is conducted to analyze the contributions of the discriminator and attention blocks within the MACGAN architecture. The results confirm that both components play significant roles in the effectiveness of MACGAN, with the discriminator ensuring adversarial training and the attention blocks effectively capturing and enhancing important image features.</p><h2>Other Information</h2><p dir="ltr">Published in: IEEE Access<br>License: <a href="http://creativecommons.org/licenses/by/4.0" target="_blank">http://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.1109/access.2023.3289591" target="_blank">https://dx.doi.org/10.1109/access.2023.3289591</a></p>2023-06-26T06:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.1109/access.2023.3289591https://figshare.com/articles/journal_contribution/MACGAN_An_All-in-One_Image_Restoration_Under_Adverse_Conditions_Using_Multidomain_Attention-Based_Conditional_GAN/25205192CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/252051922023-06-26T06:00:00Z |
| spellingShingle | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN Maria Siddiqua (17949149) Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Snow Feature extraction Rain Image restoration Image color analysis Degradation Clouds Weather forecasting Generative adversarial networks Restoration multidomain adverse weather navigation aerial marine attention mechanism |
| status_str | publishedVersion |
| title | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN |
| title_full | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN |
| title_fullStr | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN |
| title_full_unstemmed | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN |
| title_short | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN |
| title_sort | MACGAN: An All-in-One Image Restoration Under Adverse Conditions Using Multidomain Attention-Based Conditional GAN |
| topic | Engineering Electrical engineering Electronics, sensors and digital hardware Materials engineering Snow Feature extraction Rain Image restoration Image color analysis Degradation Clouds Weather forecasting Generative adversarial networks Restoration multidomain adverse weather navigation aerial marine attention mechanism |