LYT-NET: Lightweight YUV Transformer-based Network for Low-light Image Enhancement

TL;DR

LYT-Net is a lightweight transformer-based model designed for low-light image enhancement, effectively improving image quality with low complexity by utilizing novel blocks and a dual-path approach for better illumination and restoration.

Contribution

The paper introduces LYT-Net, a novel lightweight transformer architecture with unique blocks and a dual-path strategy for improved low-light image enhancement.

Findings

Outperforms recent LLIE methods on established datasets

Maintains low complexity while achieving high enhancement quality

Source code and models are publicly available

Abstract

This letter introduces LYT-Net, a novel lightweight transformer-based model for low-light image enhancement (LLIE). LYT-Net consists of several layers and detachable blocks, including our novel blocks--Channel-Wise Denoiser (CWD) and Multi-Stage Squeeze & Excite Fusion (MSEF)--along with the traditional Transformer block, Multi-Headed Self-Attention (MHSA). In our method we adopt a dual-path approach, treating chrominance channels U and V and luminance channel Y as separate entities to help the model better handle illumination adjustment and corruption restoration. Our comprehensive evaluation on established LLIE datasets demonstrates that, despite its low complexity, our model outperforms recent LLIE methods. The source code and pre-trained models are available at https://github.com/albrateanu/LYT-Net