Single-Image HDR Reconstruction by Multi-Exposure Generation

• URL: http://arxiv.org/abs/2210.15897v1
• Date: Fri, 28 Oct 2022 05:12:56 GMT
• Title: Single-Image HDR Reconstruction by Multi-Exposure Generation
• Authors: Phuoc-Hieu Le, Quynh Le, Rang Nguyen, Binh-Son Hua
• Abstract summary: We propose a weakly supervised learning method that inverts the physical image formation process for HDR reconstruction. Our neural network can invert the camera response to reconstruct pixel irradiance before synthesizing multiple exposures. Our experiments show that our proposed model can effectively reconstruct HDR images.
• Score: 8.656080193351581
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High dynamic range (HDR) imaging is an indispensable technique in modern photography. Traditional methods focus on HDR reconstruction from multiple images, solving the core problems of image alignment, fusion, and tone mapping, yet having a perfect solution due to ghosting and other visual artifacts in the reconstruction. Recent attempts at single-image HDR reconstruction show a promising alternative: by learning to map pixel values to their irradiance using a neural network, one can bypass the align-and-merge pipeline completely yet still obtain a high-quality HDR image. In this work, we propose a weakly supervised learning method that inverts the physical image formation process for HDR reconstruction via learning to generate multiple exposures from a single image. Our neural network can invert the camera response to reconstruct pixel irradiance before synthesizing multiple exposures and hallucinating details in under- and over-exposed regions from a single input image. To train the network, we propose a representation loss, a reconstruction loss, and a perceptual loss applied on pairs of under- and over-exposure images and thus do not require HDR images for training. Our experiments show that our proposed model can effectively reconstruct HDR images. Our qualitative and quantitative results show that our method achieves state-of-the-art performance on the DrTMO dataset. Our code is available at https://github.com/VinAIResearch/single_image_hdr.

Related papers

• 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)
• Learning Continuous Exposure Value Representations for Single-Image HDR Reconstruction [23.930923461672894]
  LDR stack-based methods are used for single-image HDR reconstruction, generating an HDR image from a deep learning-generated LDR stack. Current methods generate the stack with predetermined exposure values (EVs), which may limit the quality of HDR reconstruction. We propose the continuous exposure value representation (CEVR), which uses an implicit function to generate LDR images with arbitrary EVs.
  arXiv  Detail & Related papers  (2023-09-07T17:59:03Z)
• 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)
• 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)
• HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantization [39.82945546614887]
  We propose a novel learning-based approach using a spatially dynamic encoder-decoder network, HDRUNet, to learn an end-to-end mapping for single image HDR reconstruction. Our method achieves the state-of-the-art performance in quantitative comparisons and visual quality.
  arXiv  Detail & Related papers  (2021-05-27T12:12:34Z)
• Beyond Visual Attractiveness: Physically Plausible Single Image HDR Reconstruction for Spherical Panoramas [60.24132321381606]
  We introduce the physical illuminance constraints to our single-shot HDR reconstruction framework. Our method can generate HDRs which are not only visually appealing but also physically plausible.
  arXiv  Detail & Related papers  (2021-03-24T01:51:19Z)
• HDR-GAN: HDR Image Reconstruction from Multi-Exposed LDR Images with Large Motions [62.44802076971331]
  We propose a novel GAN-based model, HDR-GAN, for synthesizing HDR images from multi-exposed LDR images. By incorporating adversarial learning, our method is able to produce faithful information in the regions with missing content.
  arXiv  Detail & Related papers  (2020-07-03T11:42:35Z)
• 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)
• Single-Image HDR Reconstruction by Learning to Reverse the Camera Pipeline [100.5353614588565]
  We propose to incorporate the domain knowledge of the LDR image formation pipeline into our model. We model the HDRto-LDR image formation pipeline as the (1) dynamic range clipping, (2) non-linear mapping from a camera response function, and (3) quantization. We demonstrate that the proposed method performs favorably against state-of-the-art single-image HDR reconstruction algorithms.
  arXiv  Detail & Related papers  (2020-04-02T17:59:04Z)

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.