A Lightweight Real-Time Low-Light Enhancement Network for Embedded Automotive Vision Systems

• URL: http://arxiv.org/abs/2512.02965v1
• Date: Tue, 02 Dec 2025 17:44:25 GMT
• Title: A Lightweight Real-Time Low-Light Enhancement Network for Embedded Automotive Vision Systems
• Authors: Yuhan Chen, Yicui Shi, Guofa Li, Guangrui Bai, Jinyuan Shao, Xiangfei Huang, Wenbo Chu, Keqiang Li,
• Abstract summary: UltraFast-LieNET is a lightweight multi-scale shifted convolutional network for real-time low-light image enhancement.<n>Results on the LOLI-Street dataset show a PSNR of 26.51 dB, outperforming state-of-the-art methods by 4.6 dB while utilizing only 180 parameters.
• Score: 19.109645267196864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In low-light environments like nighttime driving, image degradation severely challenges in-vehicle camera safety. Since existing enhancement algorithms are often too computationally intensive for vehicular applications, we propose UltraFast-LieNET, a lightweight multi-scale shifted convolutional network for real-time low-light image enhancement. We introduce a Dynamic Shifted Convolution (DSConv) kernel with only 12 learnable parameters for efficient feature extraction. By integrating DSConv with varying shift distances, a Multi-scale Shifted Residual Block (MSRB) is constructed to significantly expand the receptive field. To mitigate lightweight network instability, a residual structure and a novel multi-level gradient-aware loss function are incorporated. UltraFast-LieNET allows flexible parameter configuration, with a minimum size of only 36 parameters. Results on the LOLI-Street dataset show a PSNR of 26.51 dB, outperforming state-of-the-art methods by 4.6 dB while utilizing only 180 parameters. Experiments across four benchmark datasets validate its superior balance of real-time performance and enhancement quality under limited resources. Code is available at https://githubhttps://github.com/YuhanChen2024/UltraFast-LiNET

Related papers

• Large Kernel Modulation Network for Efficient Image Super-Resolution [5.875680381119361]
  Large Kernel Modulation Network (LKMN) is a pure CNN-based model.<n>LKMN has two core components: Enhanced Partial Large Kernel Block (EPLKB) and Cross-Gate Feed-Forward Network (CGFN)<n>LKMN-L achieves 0.23 dB PSNR improvement over DAT-light on the Manga109 dataset at $times$4 upscale, with nearly $times$4.8 times faster.
  arXiv  Detail & Related papers  (2025-08-16T03:43:14Z)
• Towards Lightest Low-Light Image Enhancement Architecture for Mobile Devices [3.7651572719063178]
  Real-time low-light image enhancement on mobile and embedded devices requires models that balance visual quality and computational efficiency.<n>We propose LiteIE, an ultra-lightweight unsupervised enhancement framework that eliminates dependence on large-scale supervision.<n> LiteIE runs at 30 FPS for 4K images with just 58 parameters, enabling real-time deployment on edge devices.
  arXiv  Detail & Related papers  (2025-07-06T07:36:47Z)
• DnLUT: Ultra-Efficient Color Image Denoising via Channel-Aware Lookup Tables [60.95483707212802]
  DnLUT is an ultra-efficient lookup table-based framework that achieves high-quality color image denoising with minimal resource consumption.<n>Our key innovation lies in two complementary components: a Pairwise Channel Mixer (PCM) that effectively captures inter-channel correlations and spatial dependencies in parallel, and a novel L-shaped convolution design that maximizes receptive field coverage.<n>By converting these components into optimized lookup tables post-training, DnLUT achieves remarkable efficiency - requiring only 500KB storage and 0.1% energy consumption compared to its CNN contestant DnCNN, while delivering 20X faster inference.
  arXiv  Detail & Related papers  (2025-03-20T08:15:29Z)
• Striving for Faster and Better: A One-Layer Architecture with Auto Re-parameterization for Low-Light Image Enhancement [50.93686436282772]
  We aim to delve into the limits of image enhancers both from visual quality and computational efficiency.<n>By rethinking the task demands, we build an explicit connection, i.e., visual quality and computational efficiency are corresponding to model learning and structure design.<n>Ultimately, this achieves efficient low-light image enhancement using only a single convolutional layer, while maintaining excellent visual quality.
  arXiv  Detail & Related papers  (2025-02-27T08:20:03Z)
• RepNeXt: A Fast Multi-Scale CNN using Structural Reparameterization [8.346566205092433]
  lightweight Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) are favored for their parameter efficiency and low latency. This study investigates the complementary advantages of CNNs and ViTs to develop a versatile vision backbone tailored for resource-constrained applications.
  arXiv  Detail & Related papers  (2024-06-23T04:11:12Z)
• Binarized Low-light Raw Video Enhancement [49.65466843856074]
  Deep neural networks have achieved excellent performance on low-light raw video enhancement. In this paper, we explore the feasibility of applying the extremely compact binary neural network (BNN) to low-light raw video enhancement.
  arXiv  Detail & Related papers  (2024-03-29T02:55:07Z)
• ACNPU: A 4.75TOPS/W 1080P@30FPS Super Resolution Accelerator with Decoupled Asymmetric Convolution [0.0502254944841629]
  Deep learning-driven superresolution (SR) outperforms traditional techniques but also faces the challenge of high complexity and memory bandwidth. This paper proposes an energy-efficient SR accelerator, ACNPU, to tackle this challenge. The ACNPU enhances image quality by 0.34dB with a 27-layer model, but needs 36% less complexity than FSRCNN.
  arXiv  Detail & Related papers  (2023-08-30T07:23:32Z)
• FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table [21.77469059123589]
  We propose an efficient pipeline named FastLLVE to maintain inter-frame brightness consistency effectively. FastLLVE can process 1,080p videos at $mathit50+$ Frames Per Second (FPS), which is $mathit2 times$ faster than CNN-based methods in inference time.
  arXiv  Detail & Related papers  (2023-08-13T11:54:14Z)
• SFNet: Faster and Accurate Semantic Segmentation via Semantic Flow [88.97790684009979]
  A common practice to improve the performance is to attain high-resolution feature maps with strong semantic representation. We propose a Flow Alignment Module (FAM) to learn textitSemantic Flow between feature maps of adjacent levels. We also present a novel Gated Dual Flow Alignment Module to directly align high-resolution feature maps and low-resolution feature maps.
  arXiv  Detail & Related papers  (2022-07-10T08:25:47Z)
• FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring [72.43250555622254]
  We propose a lightweight and real-time unsupervised BID baseline, termed Frequency-domain Contrastive Loss Constrained Lightweight CycleGAN. FCL-GAN has attractive properties, i.e., no image domain limitation, no image resolution limitation, 25x lighter than SOTA, and 5x faster than SOTA. Experiments on several image datasets demonstrate the effectiveness of FCL-GAN in terms of performance, model size and reference time.
  arXiv  Detail & Related papers  (2022-04-16T15:08:03Z)
• Asymmetric CNN for image super-resolution [102.96131810686231]
  Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. We propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a mem?ory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. Our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems.
  arXiv  Detail & Related papers  (2021-03-25T07:10:46Z)
• GhostSR: Learning Ghost Features for Efficient Image Super-Resolution [49.393251361038025]
  Single image super-resolution (SISR) system based on convolutional neural networks (CNNs) achieves fancy performance while requires huge computational costs. We propose to use shift operation to generate the redundant features (i.e., Ghost features) of SISR models. We show that both the non-compact and lightweight SISR models embedded in our proposed module can achieve comparable performance to that of their baselines.
  arXiv  Detail & Related papers  (2021-01-21T10:09:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.