Enhancing Low-Light Images Using Infrared-Encoded Images

• URL: http://arxiv.org/abs/2307.04122v1
• Date: Sun, 9 Jul 2023 08:29:19 GMT
• Title: Enhancing Low-Light Images Using Infrared-Encoded Images
• Authors: Shulin Tian, Yufei Wang, Renjie Wan, Wenhan Yang, Alex C. Kot, Bihan Wen
• Abstract summary: Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss. We propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter.
• Score: 81.8710581927427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Low-light image enhancement task is essential yet challenging as it is ill-posed intrinsically. Previous arts mainly focus on the low-light images captured in the visible spectrum using pixel-wise loss, which limits the capacity of recovering the brightness, contrast, and texture details due to the small number of income photons. In this work, we propose a novel approach to increase the visibility of images captured under low-light environments by removing the in-camera infrared (IR) cut-off filter, which allows for the capture of more photons and results in improved signal-to-noise ratio due to the inclusion of information from the IR spectrum. To verify the proposed strategy, we collect a paired dataset of low-light images captured without the IR cut-off filter, with corresponding long-exposure reference images with an external filter. The experimental results on the proposed dataset demonstrate the effectiveness of the proposed method, showing better performance quantitatively and qualitatively. The dataset and code are publicly available at https://wyf0912.github.io/ELIEI/

Related papers

• You Only Need One Color Space: An Efficient Network for Low-light Image Enhancement [50.37253008333166]
  Low-Light Image Enhancement (LLIE) task tends to restore the details and visual information from corrupted low-light images. We propose a novel trainable color space, named Horizontal/Vertical-Intensity (HVI) It not only decouples brightness and color from RGB channels to mitigate the instability during enhancement but also adapts to low-light images in different illumination ranges due to the trainable parameters.
  arXiv  Detail & Related papers  (2024-02-08T16:47:43Z)
• Unsupervised network for low-light enhancement [27.052207978537098]
  Supervised networks address the task of low-light enhancement using paired images. We propose an unsupervised low-light enhancement network using contextguided illumination-adaptive norm (CIN) We also propose a region-adaptive single input multiple output (SIMO) model that can generate multiple enhanced images from a single lowlight image.
  arXiv  Detail & Related papers  (2023-06-05T13:52:08Z)
• Instance Segmentation in the Dark [43.85818645776587]
  We take a deep look at instance segmentation in the dark and introduce several techniques that substantially boost the low-light inference accuracy. We propose a novel learning method that relies on an adaptive weighted downsampling layer, a smooth-oriented convolutional block, and disturbance suppression learning. We capture a real-world low-light instance segmentation dataset comprising over two thousand paired low/normal-light images with instance-level pixel-wise annotations.
  arXiv  Detail & Related papers  (2023-04-27T16:02:29Z)
• Seeing Through The Noisy Dark: Toward Real-world Low-Light Image Enhancement and Denoising [125.56062454927755]
  Real-world low-light environment usually suffer from lower visibility and heavier noise, due to insufficient light or hardware limitation. We propose a novel end-to-end method termed Real-world Low-light Enhancement & Denoising Network (RLED-Net)
  arXiv  Detail & Related papers  (2022-10-02T14:57:23Z)
• KinD-LCE Curve Estimation And Retinex Fusion On Low-Light Image [7.280719886684936]
  This paper proposes an algorithm for low illumination enhancement. KinD-LCE uses a light curve estimation module to enhance the illumination map in the Retinex decomposed image. An illumination map and reflection map fusion module were also proposed to restore the image details and reduce detail loss.
  arXiv  Detail & Related papers  (2022-07-19T11:49:21Z)
• Enhancing Low-Light Images in Real World via Cross-Image Disentanglement [58.754943762945864]
  We propose a new low-light image enhancement dataset consisting of misaligned training images with real-world corruptions. Our model achieves state-of-the-art performances on both the newly proposed dataset and other popular low-light datasets.
  arXiv  Detail & Related papers  (2022-01-10T03:12:52Z)
• Degrade is Upgrade: Learning Degradation for Low-light Image Enhancement [52.49231695707198]
  We investigate the intrinsic degradation and relight the low-light image while refining the details and color in two steps. Inspired by the color image formulation, we first estimate the degradation from low-light inputs to simulate the distortion of environment illumination color, and then refine the content to recover the loss of diffuse illumination color. Our proposed method has surpassed the SOTA by 0.95dB in PSNR on LOL1000 dataset and 3.18% in mAP on ExDark dataset.
  arXiv  Detail & Related papers  (2021-03-19T04:00:27Z)
• Self-supervised Low Light Image Enhancement and Denoising [8.583910695494726]
  This paper proposes a self-supervised low light image enhancement method based on deep learning. It can improve the image contrast and reduce noise at the same time to avoid the blur caused by pre-/post-denoising.
  arXiv  Detail & Related papers  (2021-03-01T08:05:02Z)
• 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)
• Low-light Image Restoration with Short- and Long-exposure Raw Pairs [14.643663950015334]
  We propose a new low-light image restoration method by using the complementary information of short- and long-exposure images. We first propose a novel data generation method to synthesize realistic short- and longexposure raw images. Then, we design a new long-short-exposure fusion network (LSFNet) to deal with the problems of low-light image fusion.
  arXiv  Detail & Related papers  (2020-07-01T03:22:26Z)

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.