Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement

• URL: http://arxiv.org/abs/2110.00984v4
• Date: Thu, 7 Oct 2021 00:54:18 GMT
• Title: Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement
• Authors: Chuanjun Zheng, Daming Shi, Wentian Shi
• Abstract summary: Most existing enhancing algorithms in sRGB space only focus on the low visibility problem or suppress the noise under a hypothetical noise level. We propose an adaptive unfolding total variation network (UTVNet) to approximate the noise level from the real sRGB low-light image. Experiments on real-world low-light images clearly demonstrate the superior performance of UTVNet over state-of-the-art methods.
• Score: 6.531546527140475
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility. Since the noise exhibits different levels, its estimation has been implemented in recent works when enhancing low-light images from raw Bayer space. When it comes to sRGB color space, the noise estimation becomes more complicated due to the effect of the image processing pipeline. Nevertheless, most existing enhancing algorithms in sRGB space only focus on the low visibility problem or suppress the noise under a hypothetical noise level, leading them impractical due to the lack of robustness. To address this issue,we propose an adaptive unfolding total variation network (UTVNet), which approximates the noise level from the real sRGB low-light image by learning the balancing parameter in the model-based denoising method with total variation regularization. Meanwhile, we learn the noise level map by unrolling the corresponding minimization process for providing the inferences of smoothness and fidelity constraints. Guided by the noise level map, our UTVNet can recover finer details and is more capable to suppress noise in real captured low-light scenes. Extensive experiments on real-world low-light images clearly demonstrate the superior performance of UTVNet over state-of-the-art methods.

Related papers

• 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)
• 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)
• Zero-shot Blind Image Denoising via Implicit Neural Representations [77.79032012459243]
  We propose an alternative denoising strategy that leverages the architectural inductive bias of implicit neural representations (INRs) We show that our method outperforms existing zero-shot denoising methods under an extensive set of low-noise or real-noise scenarios.
  arXiv  Detail & Related papers  (2022-04-05T12:46:36Z)
• 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)
• 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.