Data relativistic uncertainty framework for low-illumination anime scenery image enhancement

• URL: http://arxiv.org/abs/2512.21944v1
• Date: Fri, 26 Dec 2025 09:43:24 GMT
• Title: Data relativistic uncertainty framework for low-illumination anime scenery image enhancement
• Authors: Yiquan Gao, John See,
• Abstract summary: We propose a Data Relativistic Uncertainty (DRU) framework, motivated by the idea from Relativistic GAN.<n>By analogy with the wave-particle duality of light, our framework interpretably defines and quantifies the illumination uncertainty of dark/bright samples.<n>Experiments demonstrate the effectiveness of DRU framework by training several versions of EnlightenGANs.
• Score: 9.907747671114224
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: By contrast with the prevailing works of low-light enhancement in natural images and videos, this study copes with the low-illumination quality degradation in anime scenery images to bridge the domain gap. For such an underexplored enhancement task, we first curate images from various sources and construct an unpaired anime scenery dataset with diverse environments and illumination conditions to address the data scarcity. To exploit the power of uncertainty information inherent with the diverse illumination conditions, we propose a Data Relativistic Uncertainty (DRU) framework, motivated by the idea from Relativistic GAN. By analogy with the wave-particle duality of light, our framework interpretably defines and quantifies the illumination uncertainty of dark/bright samples, which is leveraged to dynamically adjust the objective functions to recalibrate the model learning under data uncertainty. Extensive experiments demonstrate the effectiveness of DRU framework by training several versions of EnlightenGANs, yielding superior perceptual and aesthetic qualities beyond the state-of-the-art methods that are incapable of learning from data uncertainty perspective. We hope our framework can expose a novel paradigm of data-centric learning for potential visual and language domains. Code is available.

Related papers

• LuxDiT: Lighting Estimation with Video Diffusion Transformer [66.60450792095901]
  Estimating scene lighting from a single image or video remains a longstanding challenge in computer vision and graphics.<n>We propose LuxDiT, a novel data-driven approach that fine-tunes a video diffusion transformer to generate HDR environment maps conditioned on visual input.
  arXiv  Detail & Related papers  (2025-09-03T19:59:20Z)
• Nonlocal Retinex-Based Variational Model and its Deep Unfolding Twin for Low-Light Image Enhancement [3.174882428337821]
  We propose a variational method for low-light image enhancement based on the Retinex decomposition.<n>A color correction pre-processing step is applied to the low-light image, which is then used as the observed input in the decomposition.<n>We extend the model by introducing its deep unfolding counterpart, in which the operators are replaced with learnable networks.
  arXiv  Detail & Related papers  (2025-04-10T14:48:26Z)
• Leveraging Content and Context Cues for Low-Light Image Enhancement [25.97198463881292]
  Low-light conditions have an adverse impact on machine cognition, limiting the performance of computer vision systems in real life.<n>We propose to improve the existing zero-reference low-light enhancement by leveraging the CLIP model to capture image prior and for semantic guidance.<n>We experimentally show, that the proposed prior and semantic guidance help to improve the overall image contrast and hue, as well as improve background-foreground discrimination.
  arXiv  Detail & Related papers  (2024-12-10T17:32:09Z)
• Data Augmentation via Latent Diffusion for Saliency Prediction [67.88936624546076]
  Saliency prediction models are constrained by the limited diversity and quantity of labeled data. We propose a novel data augmentation method for deep saliency prediction that edits natural images while preserving the complexity and variability of real-world scenes.
  arXiv  Detail & Related papers  (2024-09-11T14:36:24Z)
• Illumination and Shadows in Head Rotation: experiments with Denoising Diffusion Models [0.0]
  This study delves into the latent space of denoising diffusion models to identify compelling trajectories that can express continuous head rotation under varying lighting conditions.<n>A key contribution of our work is the generation of additional labels from the CelebA dataset,categorizing images into three groups based on prevalent illumination direction.
  arXiv  Detail & Related papers  (2023-08-11T10:14:22Z)
• Bilevel Fast Scene Adaptation for Low-Light Image Enhancement [50.639332885989255]
  Enhancing images in low-light scenes is a challenging but widely concerned task in the computer vision. Main obstacle lies in the modeling conundrum from distribution discrepancy across different scenes. We introduce the bilevel paradigm to model the above latent correspondence. A bilevel learning framework is constructed to endow the scene-irrelevant generality of the encoder towards diverse scenes.
  arXiv  Detail & Related papers  (2023-06-02T08:16:21Z)
• 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)
• TensoIR: Tensorial Inverse Rendering [51.57268311847087]
  TensoIR is a novel inverse rendering approach based on tensor factorization and neural fields. TensoRF is a state-of-the-art approach for radiance field modeling.
  arXiv  Detail & Related papers  (2023-04-24T21:39:13Z)
• DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer [78.91753256634453]
  We consider the challenging problem of predicting intrinsic object properties from a single image by exploiting differentiables. In this work, we propose DIBR++, a hybrid differentiable which supports these effects by combining specularization and ray-tracing. Compared to more advanced physics-based differentiables, DIBR++ is highly performant due to its compact and expressive model.
  arXiv  Detail & Related papers  (2021-10-30T01:59:39Z)
• NOD: Taking a Closer Look at Detection under Extreme Low-Light Conditions with Night Object Detection Dataset [25.29013780731876]
  Low light proves more difficult for machine cognition than previously thought. We present a large-scale dataset showing dynamic scenes captured on the streets at night. We propose to incorporate an image enhancement module into the object detection framework and two novel data augmentation techniques.
  arXiv  Detail & Related papers  (2021-10-20T03:44:04Z)
• 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.