HDR Image Reconstruction using an Unsupervised Fusion Model

• URL: http://arxiv.org/abs/2510.21815v1
• Date: Tue, 21 Oct 2025 17:43:22 GMT
• Title: HDR Image Reconstruction using an Unsupervised Fusion Model
• Authors: Kumbha Nagaswetha,
• Abstract summary: High Dynamic Range (Exposure) imaging aims to reproduce the wide range of brightness levels present in natural scenes.<n>We propose a deep learning-based multi-exposure fusion approach for HDR image generation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: High Dynamic Range (HDR) imaging aims to reproduce the wide range of brightness levels present in natural scenes, which the human visual system can perceive but conventional digital cameras often fail to capture due to their limited dynamic range. To address this limitation, we propose a deep learning-based multi-exposure fusion approach for HDR image generation. The method takes a set of differently exposed Low Dynamic Range (LDR) images, typically an underexposed and an overexposed image, and learns to fuse their complementary information using a convolutional neural network (CNN). The underexposed image preserves details in bright regions, while the overexposed image retains information in dark regions; the network effectively combines these to reconstruct a high-quality HDR output. The model is trained in an unsupervised manner, without relying on ground-truth HDR images, making it practical for real-world applications where such data is unavailable. We evaluate our results using the Multi-Exposure Fusion Structural Similarity Index Measure (MEF-SSIM) and demonstrate that our approach achieves superior visual quality compared to existing fusion methods. A customized loss function is further introduced to improve reconstruction fidelity and optimize model performance.

Related papers

• Reconstructing 3D Scenes in Native High Dynamic Range [82.90064638813185]
  We present the first method for 3D scene reconstruction that directly models native HDR observations.<n>We propose bf Native High dynamic range 3D Gaussian Splatting (NH-3DGS), which preserves the full dynamic range throughout the reconstruction pipeline.<n>We demonstrate on both synthetic and real multi-view HDR datasets that NH-3DGS significantly outperforms existing methods in reconstruction quality and dynamic range preservation.
  arXiv  Detail & Related papers  (2025-11-17T02:33:31Z)
• Generating Content for HDR Deghosting from Frequency View [56.103761824603644]
  Recent Diffusion Models (DMs) have been introduced in HDR imaging field. DMs require extensive iterations with large models to estimate entire images. We propose the Low-Frequency aware Diffusion (LF-Diff) model for ghost-free HDR imaging.
  arXiv  Detail & Related papers  (2024-04-01T01:32:11Z)
• Towards High-quality HDR Deghosting with Conditional Diffusion Models [88.83729417524823]
  High Dynamic Range (LDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. DNNs still generate ghosting artifacts when LDR images have saturation and large motion. We formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition.
  arXiv  Detail & Related papers  (2023-11-02T01:53:55Z)
• Self-Supervised High Dynamic Range Imaging with Multi-Exposure Images in Dynamic Scenes [58.66427721308464]
  Self is a self-supervised reconstruction method that only requires dynamic multi-exposure images during training. Self achieves superior results against the state-of-the-art self-supervised methods, and comparable performance to supervised ones.
  arXiv  Detail & Related papers  (2023-10-03T07:10:49Z)
• SMAE: Few-shot Learning for HDR Deghosting with Saturation-Aware Masked Autoencoders [97.64072440883392]
  We propose a novel semi-supervised approach to realize few-shot HDR imaging via two stages of training, called SSHDR. Unlikely previous methods, directly recovering content and removing ghosts simultaneously, which is hard to achieve optimum. Experiments demonstrate that SSHDR outperforms state-of-the-art methods quantitatively and qualitatively within and across different datasets.
  arXiv  Detail & Related papers  (2023-04-14T03:42:51Z)
• Multi-Exposure HDR Composition by Gated Swin Transformer [8.619880437958525]
  This paper provides a novel multi-exposure fusion model based on Swin Transformer. We exploit the long distance contextual dependency in the exposure-space pyramid by the self-attention mechanism. Experiments show that our model achieves the accuracy on par with current top performing multi-exposure HDR imaging models.
  arXiv  Detail & Related papers  (2023-03-15T15:38:43Z)
• GlowGAN: Unsupervised Learning of HDR Images from LDR Images in the Wild [74.52723408793648]
  We present the first method for learning a generative model of HDR images from in-the-wild LDR image collections in a fully unsupervised manner. The key idea is to train a generative adversarial network (GAN) to generate HDR images which, when projected to LDR under various exposures, are indistinguishable from real LDR images. Experiments show that our method GlowGAN can synthesize photorealistic HDR images in many challenging cases such as landscapes, lightning, or windows.
  arXiv  Detail & Related papers  (2022-11-22T15:42:08Z)
• FlexHDR: Modelling Alignment and Exposure Uncertainties for Flexible HDR Imaging [0.9185931275245008]
  We present a new HDR imaging technique that models alignment and exposure uncertainties to produce high quality HDR results. We introduce a strategy that learns to jointly align and assess the alignment and exposure reliability using an HDR-aware, uncertainty-driven attention map. Experimental results show our method can produce better quality HDR images with up to 0.8dB PSNR improvement to the state-of-the-art.
  arXiv  Detail & Related papers  (2022-01-07T14:27:17Z)
• HDR-cGAN: Single LDR to HDR Image Translation using Conditional GAN [24.299931323012757]
  Low Dynamic Range (LDR) cameras are incapable of representing the wide dynamic range of the real-world scene. We propose a deep learning based approach to recover details in the saturated areas while reconstructing the HDR image. We present a novel conditional GAN (cGAN) based framework trained in an end-to-end fashion over the HDR-REAL and HDR-SYNTH datasets.
  arXiv  Detail & Related papers  (2021-10-04T18:50:35Z)
• End-to-End Differentiable Learning to HDR Image Synthesis for Multi-exposure Images [23.895981099137533]
  High dynamic range () image reconstruction based on the multiple exposure stack from a given single exposure utilizes a deep learning framework to generate high-quality HDR images. We tackle the problem in stack reconstruction-based methods by proposing a novel framework with a fully differentiable high dynamic range imaging (I) process. In other words, our differentiable HDR synthesis layer helps the deep neural network to train to create multi-exposure stacks while reflecting the precise correlations between multi-exposure images in the HDRI process.
  arXiv  Detail & Related papers  (2020-06-29T06:47:07Z)

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.