Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping

• URL: http://arxiv.org/abs/2310.17190v2
• Date: Wed, 3 Jan 2024 11:44:30 GMT
• Title: Lookup Table meets Local Laplacian Filter: Pyramid Reconstruction Network for Tone Mapping
• Authors: Feng Zhang, Ming Tian, Zhiqiang Li, Bin Xu, Qingbo Lu, Changxin Gao, and Nong Sang
• Abstract summary: This paper explores a novel strategy that integrates global and local operators by utilizing closed-form Laplacian pyramid decomposition and reconstruction. We employ image-adaptive 3D LUTs to manipulate the tone in the low-frequency image by leveraging the specific characteristics of the frequency information. We also utilize local Laplacian filters to refine the edge details in the high-frequency components in an adaptive manner.
• Score: 35.47139372780014
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Tone mapping aims to convert high dynamic range (HDR) images to low dynamic range (LDR) representations, a critical task in the camera imaging pipeline. In recent years, 3-Dimensional LookUp Table (3D LUT) based methods have gained attention due to their ability to strike a favorable balance between enhancement performance and computational efficiency. However, these methods often fail to deliver satisfactory results in local areas since the look-up table is a global operator for tone mapping, which works based on pixel values and fails to incorporate crucial local information. To this end, this paper aims to address this issue by exploring a novel strategy that integrates global and local operators by utilizing closed-form Laplacian pyramid decomposition and reconstruction. Specifically, we employ image-adaptive 3D LUTs to manipulate the tone in the low-frequency image by leveraging the specific characteristics of the frequency information. Furthermore, we utilize local Laplacian filters to refine the edge details in the high-frequency components in an adaptive manner. Local Laplacian filters are widely used to preserve edge details in photographs, but their conventional usage involves manual tuning and fixed implementation within camera imaging pipelines or photo editing tools. We propose to learn parameter value maps progressively for local Laplacian filters from annotated data using a lightweight network. Our model achieves simultaneous global tone manipulation and local edge detail preservation in an end-to-end manner. Extensive experimental results on two benchmark datasets demonstrate that the proposed method performs favorably against state-of-the-art methods.

Related papers

• FaVoR: Features via Voxel Rendering for Camera Relocalization [23.7893950095252]
  Camera relocalization methods range from dense image alignment to direct camera pose regression from a query image. We propose a novel approach that leverages a globally sparse yet locally dense 3D representation of 2D features. By tracking and triangulating landmarks over a sequence of frames, we construct a sparse voxel map optimized to render image patch descriptors observed during tracking.
  arXiv  Detail & Related papers  (2024-09-11T18:58:16Z)
• Visual Localization in 3D Maps: Comparing Point Cloud, Mesh, and NeRF Representations [8.522160106746478]
  We present a global visual localization system capable of localizing a single camera image across various 3D map representations. Our system generates a database by synthesizing novel views of the scene, creating RGB and depth image pairs. NeRF synthesized images show superior performance, localizing query images at an average success rate of 72%.
  arXiv  Detail & Related papers  (2024-08-21T19:37:17Z)
• Boosting Cross-Domain Point Classification via Distilling Relational Priors from 2D Transformers [59.0181939916084]
  Traditional 3D networks mainly focus on local geometric details and ignore the topological structure between local geometries. We propose a novel Priors Distillation (RPD) method to extract priors from the well-trained transformers on massive images. Experiments on the PointDA-10 and the Sim-to-Real datasets verify that the proposed method consistently achieves the state-of-the-art performance of UDA for point cloud classification.
  arXiv  Detail & Related papers  (2024-07-26T06:29:09Z)
• GLFNET: Global-Local (frequency) Filter Networks for efficient medical image segmentation [18.314093733807972]
  We propose a transformer-style architecture called Global-Local Filter Network (GLFNet) for medical image segmentation. We replace the self-attention mechanism with a combination of global-local filter blocks to optimize model efficiency. We test GLFNet on three benchmark datasets achieving state-of-the-art performance on all of them while being almost twice as efficient in terms of GFLOP operations.
  arXiv  Detail & Related papers  (2024-03-01T09:35:03Z)
• GS-SLAM: Dense Visual SLAM with 3D Gaussian Splatting [51.96353586773191]
  We introduce textbfGS-SLAM that first utilizes 3D Gaussian representation in the Simultaneous Localization and Mapping system. Our method utilizes a real-time differentiable splatting rendering pipeline that offers significant speedup to map optimization and RGB-D rendering. Our method achieves competitive performance compared with existing state-of-the-art real-time methods on the Replica, TUM-RGBD datasets.
  arXiv  Detail & Related papers  (2023-11-20T12:08:23Z)
• Frequency Perception Network for Camouflaged Object Detection [51.26386921922031]
  We propose a novel learnable and separable frequency perception mechanism driven by the semantic hierarchy in the frequency domain. Our entire network adopts a two-stage model, including a frequency-guided coarse localization stage and a detail-preserving fine localization stage. Compared with the currently existing models, our proposed method achieves competitive performance in three popular benchmark datasets.
  arXiv  Detail & Related papers  (2023-08-17T11:30:46Z)
• LATITUDE: Robotic Global Localization with Truncated Dynamic Low-pass Filter in City-scale NeRF [5.364698641882657]
  We present a two-stage localization mechanism in city-scale Neural Radiance Fields (NeRF) In place recognition stage, we train a regressor through images generated from trained NeRFs, which provides an initial value for global localization. In pose optimization stage, we minimize the residual between the observed image and rendered image by directly optimizing the pose on tangent plane. We evaluate our method on both synthetic and real-world data and show its potential applications for high-precision navigation in large-scale city scenes.
  arXiv  Detail & Related papers  (2022-09-18T07:56:06Z)
• LODE: Deep Local Deblurring and A New Benchmark [106.80269193462712]
  We propose a novel framework, termed BLur-Aware DEblurring network (BladeNet) BladeNet contains three components: the Local Blur Synthesis module generates locally blurred training pairs, the Local Blur Perception module automatically captures the locally blurred region and the Blur-guided Spatial Attention module guides the deblurring network with spatial attention. We carry out extensive experiments on REDS and LODE datasets showing that BladeNet improves PSNR by 2.5dB over SotAs for local deblurring while keeping comparable performance for global deblurring.
  arXiv  Detail & Related papers  (2021-09-19T15:57:28Z)
• Unsupervised Metric Relocalization Using Transform Consistency Loss [66.19479868638925]
  Training networks to perform metric relocalization traditionally requires accurate image correspondences. We propose a self-supervised solution, which exploits a key insight: localizing a query image within a map should yield the same absolute pose, regardless of the reference image used for registration. We evaluate our framework on synthetic and real-world data, showing our approach outperforms other supervised methods when a limited amount of ground-truth information is available.
  arXiv  Detail & Related papers  (2020-11-01T19:24:27Z)
• SPAN: Spatial Pyramid Attention Network forImage Manipulation Localization [24.78951727072683]
  We present a novel framework, Spatial Pyramid Attention Network (SPAN) for detection and localization of multiple types of image manipulations. SPAN is trained on a generic, synthetic dataset but can also be fine tuned for specific datasets. The proposed method shows significant gains in performance on standard datasets over previous state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-09-01T21:59:35Z)

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.