LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement

• URL: http://arxiv.org/abs/2502.15186v1
• Date: Fri, 21 Feb 2025 03:37:58 GMT
• Title: LUMINA-Net: Low-light Upgrade through Multi-stage Illumination and Noise Adaptation Network for Image Enhancement
• Authors: Namrah Siddiqua, Kim Suneung,
• Abstract summary: Low-light image enhancement (LLIE) is a crucial task in computer vision aimed to enhance the visual fidelity of images captured under low-illumination conditions.<n>We propose LUMINA-Net an advanced deep learning framework designed specifically by integrating multi-stage illumination and reflectance modules.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Low-light image enhancement (LLIE) is a crucial task in computer vision aimed to enhance the visual fidelity of images captured under low-illumination conditions. Conventional methods frequently struggle to mitigate pervasive shortcomings such as noise, over-exposure, and color distortion thereby precipitating a pronounced degradation in image quality. To address these challenges, we propose LUMINA-Net an advanced deep learning framework designed specifically by integrating multi-stage illumination and reflectance modules. First, the illumination module intelligently adjusts brightness and contrast levels while meticulously preserving intricate textural details. Second, the reflectance module incorporates a noise reduction mechanism that leverages spatial attention and channel-wise feature refinement to mitigate noise contamination. Through a comprehensive suite of experiments conducted on LOL and SICE datasets using PSNR, SSIM and LPIPS metrics, surpassing state-of-the-art methodologies and showcasing its efficacy in low-light image enhancement.

Related papers

• CodeEnhance: A Codebook-Driven Approach for Low-Light Image Enhancement [97.95330185793358]
  Low-light image enhancement (LLIE) aims to improve low-illumination images. Existing methods face two challenges: uncertainty in restoration from diverse brightness degradations and loss of texture and color information. We propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement.
  arXiv  Detail & Related papers  (2024-04-08T07:34:39Z)
• LDM-ISP: Enhancing Neural ISP for Low Light with Latent Diffusion Models [54.93010869546011]
  We propose to leverage the pre-trained latent diffusion model to perform the neural ISP for enhancing extremely low-light images. Specifically, to tailor the pre-trained latent diffusion model to operate on the RAW domain, we train a set of lightweight taming modules. We observe different roles of UNet denoising and decoder reconstruction in the latent diffusion model, which inspires us to decompose the low-light image enhancement task into latent-space low-frequency content generation and decoding-phase high-frequency detail maintenance.
  arXiv  Detail & Related papers  (2023-12-02T04:31:51Z)
• Advancing Unsupervised Low-light Image Enhancement: Noise Estimation, Illumination Interpolation, and Self-Regulation [55.07472635587852]
  Low-Light Image Enhancement (LLIE) techniques have made notable advancements in preserving image details and enhancing contrast. These approaches encounter persistent challenges in efficiently mitigating dynamic noise and accommodating diverse low-light scenarios. We first propose a method for estimating the noise level in low light images in a quick and accurate way. We then devise a Learnable Illumination Interpolator (LII) to satisfy general constraints between illumination and input.
  arXiv  Detail & Related papers  (2023-05-17T13:56:48Z)
• Cycle-Interactive Generative Adversarial Network for Robust Unsupervised Low-Light Enhancement [109.335317310485]
  Cycle-Interactive Generative Adversarial Network (CIGAN) is capable of not only better transferring illumination distributions between low/normal-light images but also manipulating detailed signals. In particular, the proposed low-light guided transformation feed-forwards the features of low-light images from the generator of enhancement GAN into the generator of degradation GAN.
  arXiv  Detail & Related papers  (2022-07-03T06:37:46Z)
• An Effective Image Restorer: Denoising and Luminance Adjustment for Low-photon-count Imaging [6.358214877782411]
  raw image restoration under low-photon-count conditions by simulating the imaging of quanta image sensor (QIS) We develop a lightweight framework, which consists of a multi-level pyramid denoising network (MPDNet) and a luminance adjustment (LA) module to achieve separate denoising and luminance enhancement. Our image restorer can achieve superior performance on the degraded images with various photon levels by suppressing noise and recovering luminance and color effectively.
  arXiv  Detail & Related papers  (2021-10-29T12:16:30Z)
• CERL: A Unified Optimization Framework for Light Enhancement with Realistic Noise [81.47026986488638]
  Low-light images captured in the real world are inevitably corrupted by sensor noise. Existing light enhancement methods either overlook the important impact of real-world noise during enhancement, or treat noise removal as a separate pre- or post-processing step. We present Coordinated Enhancement for Real-world Low-light Noisy Images (CERL), that seamlessly integrates light enhancement and noise suppression parts into a unified and physics-grounded framework.
  arXiv  Detail & Related papers  (2021-08-01T15:31:15Z)
• Progressive Joint Low-light Enhancement and Noise Removal for Raw Images [10.778200442212334]
  Low-light imaging on mobile devices is typically challenging due to insufficient incident light coming through the relatively small aperture. We propose a low-light image processing framework that performs joint illumination adjustment, color enhancement, and denoising. Our framework does not need to recollect massive data when being adapted to another camera model.
  arXiv  Detail & Related papers  (2021-06-28T16:43:52Z)
• Low-Light Maritime Image Enhancement with Regularized Illumination Optimization and Deep Noise Suppression [5.401654133604235]
  We propose to enhance the low-light images through regularized illumination optimization and deep noise suppression. Comprehensive experiments have been conducted on both synthetic and realistic maritime images to compare our proposed method with several state-of-the-art imaging methods.
  arXiv  Detail & Related papers  (2020-08-09T17:05:23Z)
• Deep Bilateral Retinex for Low-Light Image Enhancement [96.15991198417552]
  Low-light images suffer from poor visibility caused by low contrast, color distortion and measurement noise. This paper proposes a deep learning method for low-light image enhancement with a particular focus on handling the measurement noise. The proposed method is very competitive to the state-of-the-art methods, and has significant advantage over others when processing images captured in extremely low lighting conditions.
  arXiv  Detail & Related papers  (2020-07-04T06:26:44Z)
• Unsupervised Low-light Image Enhancement with Decoupled Networks [103.74355338972123]
  We learn a two-stage GAN-based framework to enhance the real-world low-light images in a fully unsupervised fashion. Our proposed method outperforms the state-of-the-art unsupervised image enhancement methods in terms of both illumination enhancement and noise reduction.
  arXiv  Detail & Related papers  (2020-05-06T13:37:08Z)

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.