Work

DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement

Year: 2025

Type: article

Abstract: 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.

Source: Sensors

DOI: 10.3390/s25051530

Authors: Raul Balmez, Alexandru Brateanu, Ciprian Orhei, Codruta O. Ancuti, Cosmin Ancuti

Institutions: University of Manchester, Politehnica University of Timisoara

Language: English

Topic: Image Enhancement Techniques

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $3,137