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DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement
Raul Balmez1, Alexandru Brateanu1, Ciprian Orhei2
1Department of Computer Science, University of Manchester, Manchester M13 9PL, UK.
Sensors (Basel, Switzerland)
|March 17, 2025
Summary
This study introduces an efficient transformer-based framework for low-light image enhancement. The novel design improves performance and reduces computational load for better low-light computer vision.
Area of Science:
- Computer Vision
- Image Processing
Background:
- Low-light image enhancement faces challenges like noise, low contrast, and color distortion.
- Computational demands for processing spatial dependencies in low-light images are significant.
Purpose of the Study:
- To present a novel, efficient transformer-based framework for low-light image enhancement.
- To reduce computational overhead while maintaining high performance in image enhancement.
Main Methods:
- Utilized a transformer-based framework incorporating depthwise separable convolutions.
- Developed an original feed-forward network design to minimize computational requirements.
Main Results:
- The proposed method achieves competitive results in low-light image enhancement.
- Demonstrated practical and effective enhancement for images captured in low-light conditions.
Conclusions:
- The novel transformer framework offers an efficient and effective solution for low-light image enhancement.
- The integration of depthwise separable convolutions and a new feed-forward network design addresses computational challenges.
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