DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement
Low-light image enhancement is an important task in computer vision, often made challenging by the limitations of image sensors, such as noise, low contrast, and color distortion. These challenges are further exacerbated by the computational demands of processing spatial dependencies under such cond...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-03-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/5/1530 |
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| _version_ | 1850053040702226432 |
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| author | Raul Balmez Alexandru Brateanu Ciprian Orhei Codruta O. Ancuti Cosmin Ancuti |
| author_facet | Raul Balmez Alexandru Brateanu Ciprian Orhei Codruta O. Ancuti Cosmin Ancuti |
| author_sort | Raul Balmez |
| collection | DOAJ |
| description | Low-light image enhancement is an important task in computer vision, often made challenging by the limitations of image sensors, such as noise, low contrast, and color distortion. These challenges are further exacerbated by the computational demands of processing spatial dependencies under such conditions. We present a novel transformer-based framework that enhances efficiency by utilizing depthwise separable convolutions instead of conventional approaches. Additionally, an original feed-forward network design reduces the computational overhead while maintaining high performance. Experimental results demonstrate that this method achieves competitive results, providing a practical and effective solution for enhancing images captured in low-light environments. |
| format | Article |
| id | doaj-art-bce5c69721fc4aeca4e7a1093bb1cbf7 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-bce5c69721fc4aeca4e7a1093bb1cbf72025-08-20T02:52:38ZengMDPI AGSensors1424-82202025-03-01255153010.3390/s25051530DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image EnhancementRaul Balmez0Alexandru Brateanu1Ciprian Orhei2Codruta O. Ancuti3Cosmin Ancuti4Department of Computer Science, University of Manchester, Manchester M13 9PL, UKDepartment of Computer Science, University of Manchester, Manchester M13 9PL, UKFaculty of Electronics, Telecommunications and Information Technologies, Polytechnic University Timisoara, 300223 Timisoara, RomaniaFaculty of Electronics, Telecommunications and Information Technologies, Polytechnic University Timisoara, 300223 Timisoara, RomaniaFaculty of Electronics, Telecommunications and Information Technologies, Polytechnic University Timisoara, 300223 Timisoara, RomaniaLow-light image enhancement is an important task in computer vision, often made challenging by the limitations of image sensors, such as noise, low contrast, and color distortion. These challenges are further exacerbated by the computational demands of processing spatial dependencies under such conditions. We present a novel transformer-based framework that enhances efficiency by utilizing depthwise separable convolutions instead of conventional approaches. Additionally, an original feed-forward network design reduces the computational overhead while maintaining high performance. Experimental results demonstrate that this method achieves competitive results, providing a practical and effective solution for enhancing images captured in low-light environments.https://www.mdpi.com/1424-8220/25/5/1530image sensor restorationlow-light enhancementvision transformer |
| spellingShingle | Raul Balmez Alexandru Brateanu Ciprian Orhei Codruta O. Ancuti Cosmin Ancuti DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement Sensors image sensor restoration low-light enhancement vision transformer |
| title | DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement |
| title_full | DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement |
| title_fullStr | DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement |
| title_full_unstemmed | DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement |
| title_short | DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement |
| title_sort | depthlux employing depthwise separable convolutions for low light image enhancement |
| topic | image sensor restoration low-light enhancement vision transformer |
| url | https://www.mdpi.com/1424-8220/25/5/1530 |
| work_keys_str_mv | AT raulbalmez depthluxemployingdepthwiseseparableconvolutionsforlowlightimageenhancement AT alexandrubrateanu depthluxemployingdepthwiseseparableconvolutionsforlowlightimageenhancement AT ciprianorhei depthluxemployingdepthwiseseparableconvolutionsforlowlightimageenhancement AT codrutaoancuti depthluxemployingdepthwiseseparableconvolutionsforlowlightimageenhancement AT cosminancuti depthluxemployingdepthwiseseparableconvolutionsforlowlightimageenhancement |