ArticleSensors (Basel, Switzerland)2025
DepthLux: Employing Depthwise Separable Convolutions for Low-Light Image Enhancement.
Article in Sensors (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- CBAM meets DropBlock: enhancing robot steering-angle prediction with hybrid attention and structured dropout.Scientific reports · 2026Article
- DLFE-Net: Preserving Details and Removing Noise Using HVI Color Space for Low-Light Image Enhancement.Sensors (Basel, Switzerland) · 2025Article
Corrections and comments
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Authors and funding
5 authors.
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No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.