Balmez Raul, Brateanu Alexandru, Orhei Ciprian, Ancuti Codruta O, Ancuti Cosmin
Department of Computer Science, University of Manchester, Manchester M13 9PL, UK.
Faculty of Electronics, Telecommunications and Information Technologies, Polytechnic University Timisoara, 300223 Timisoara, Romania.
Sensors (Basel). 2025 Mar 1;25(5):1530. doi: 10.3390/s25051530.
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.
低光图像增强是计算机视觉中的一项重要任务，由于图像传感器的局限性，如噪声、低对比度和颜色失真，这项任务常常具有挑战性。在这种条件下处理空间依赖性的计算需求进一步加剧了这些挑战。我们提出了一种基于新型变压器的框架，该框架通过使用深度可分离卷积而不是传统方法来提高效率。此外，一种原创的前馈网络设计在保持高性能的同时减少了计算开销。实验结果表明，该方法取得了具有竞争力的结果，为增强在低光环境中拍摄的图像提供了一种实用有效的解决方案。
