Unsupervised Learning Based Multi-Scale Exposure Fusion

• URL: http://arxiv.org/abs/2409.17830v1
• Date: Thu, 26 Sep 2024 13:29:40 GMT
• Title: Unsupervised Learning Based Multi-Scale Exposure Fusion
• Authors: Chaobing Zheng, Shiqian Wu, Zhenggguo Li,
• Abstract summary: Unsupervised learning based multi-scale exposure fusion (ULMEF) is efficient for fusing differently exposed low dynamic range (LDR) images into a higher quality LDR image for a high dynamic range scene. In this paper, novel loss functions are proposed for the ULMEF and they are defined by using all the images to be fused and other differently exposed images from the same HDR scene.
• Score: 9.152843503286796
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Unsupervised learning based multi-scale exposure fusion (ULMEF) is efficient for fusing differently exposed low dynamic range (LDR) images into a higher quality LDR image for a high dynamic range (HDR) scene. Unlike supervised learning, loss functions play a crucial role in the ULMEF. In this paper, novel loss functions are proposed for the ULMEF and they are defined by using all the images to be fused and other differently exposed images from the same HDR scene. The proposed loss functions can guide the proposed ULMEF to learn more reliable information from the HDR scene than existing loss functions which are defined by only using the set of images to be fused. As such, the quality of the fused image is significantly improved. The proposed ULMEF also adopts a multi-scale strategy that includes a multi-scale attention module to effectively preserve the scene depth and local contrast in the fused image. Meanwhile, the proposed ULMEF can be adopted to achieve exposure interpolation and exposure extrapolation. Extensive experiments show that the proposed ULMEF algorithm outperforms state-of-the-art exposure fusion algorithms.

Related papers

• Reti-Diff: Illumination Degradation Image Restoration with Retinex-based Latent Diffusion Model [59.08821399652483]
  Illumination degradation image restoration (IDIR) techniques aim to improve the visibility of degraded images and mitigate the adverse effects of deteriorated illumination. Among these algorithms, diffusion model (DM)-based methods have shown promising performance but are often burdened by heavy computational demands and pixel misalignment issues when predicting the image-level distribution. We propose to leverage DM within a compact latent space to generate concise guidance priors and introduce a novel solution called Reti-Diff for the IDIR task. Reti-Diff comprises two key components: the Retinex-based latent DM (RLDM) and the Retinex-guided transformer (RG
  arXiv  Detail & Related papers  (2023-11-20T09:55:06Z)
• Bridging the Gap between Multi-focus and Multi-modal: A Focused Integration Framework for Multi-modal Image Fusion [5.417493475406649]
  Multi-modal image fusion (MMIF) integrates valuable information from different modality images into a fused one. This paper proposes a MMIF framework for joint focused integration and modalities information extraction. The proposed algorithm can surpass the state-of-the-art methods in visual perception and quantitative evaluation.
  arXiv  Detail & Related papers  (2023-11-03T12:58:39Z)
• Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion [60.221404321514086]
  Multi-exposure image fusion (MEF) has emerged as a prominent solution to address the limitations of digital imaging in representing varied exposure levels. This paper presents a Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which introduces a bi-level optimization search scheme for automatic design of both network structures and loss functions.
  arXiv  Detail & Related papers  (2023-09-03T08:07:26Z)
• Enhancing Low-light Light Field Images with A Deep Compensation Unfolding Network [52.77569396659629]
  This paper presents the deep compensation network unfolding (DCUNet) for restoring light field (LF) images captured under low-light conditions. The framework uses the intermediate enhanced result to estimate the illumination map, which is then employed in the unfolding process to produce a new enhanced result. To properly leverage the unique characteristics of LF images, this paper proposes a pseudo-explicit feature interaction module.
  arXiv  Detail & Related papers  (2023-08-10T07:53:06Z)
• Single Image LDR to HDR Conversion using Conditional Diffusion [18.466814193413487]
  Digital imaging aims to replicate realistic scenes, but Low Dynamic Range (LDR) cameras cannot represent the wide dynamic range of real scenes. This paper presents a deep learning-based approach for recovering intricate details from shadows and highlights. We incorporate a deep-based autoencoder in our proposed framework to enhance the quality of the latent representation of LDR image used for conditioning.
  arXiv  Detail & Related papers  (2023-07-06T07:19:47Z)
• Denoising Diffusion Models for Plug-and-Play Image Restoration [135.6359475784627]
  This paper proposes DiffPIR, which integrates the traditional plug-and-play method into the diffusion sampling framework. Compared to plug-and-play IR methods that rely on discriminative Gaussian denoisers, DiffPIR is expected to inherit the generative ability of diffusion models.
  arXiv  Detail & Related papers  (2023-05-15T20:24:38Z)
• Fully Self-Supervised Depth Estimation from Defocus Clue [79.63579768496159]
  We propose a self-supervised framework that estimates depth purely from a sparse focal stack. We show that our framework circumvents the needs for the depth and AIF image ground-truth, and receives superior predictions.
  arXiv  Detail & Related papers  (2023-03-19T19:59:48Z)
• Wild ToFu: Improving Range and Quality of Indirect Time-of-Flight Depth with RGB Fusion in Challenging Environments [56.306567220448684]
  We propose a new learning based end-to-end depth prediction network which takes noisy raw I-ToF signals as well as an RGB image. We show more than 40% RMSE improvement on the final depth map compared to the baseline approach.
  arXiv  Detail & Related papers  (2021-12-07T15:04:14Z)
• PAS-MEF: Multi-exposure image fusion based on principal component analysis, adaptive well-exposedness and saliency map [0.0]
  With regular low dynamic range (LDR) capture/display devices, significant details may not be preserved in images due to the huge dynamic range of natural scenes. This study proposes an efficient multi-exposure fusion (MEF) approach with a simple yet effective weight extraction method. Experimental comparisons with existing techniques demonstrate that the proposed method produces very strong statistical and visual results.
  arXiv  Detail & Related papers  (2021-05-25T10:22:43Z)
• 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)
• Extreme Low-Light Imaging with Multi-granulation Cooperative Networks [18.438827277749525]
  Low-light imaging is challenging since images may appear to be dark and noised due to low signal-to-noise ratio, complex image content, and variety in shooting scenes in extreme low-light condition. Many methods have been proposed to enhance the imaging quality under extreme low-light conditions, but it remains difficult to obtain satisfactory results.
  arXiv  Detail & Related papers  (2020-05-16T14:26:06Z)

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.