Wavelet-Based Network For High Dynamic Range Imaging

• URL: http://arxiv.org/abs/2108.01434v3
• Date: Tue, 7 Nov 2023 23:00:36 GMT
• Title: Wavelet-Based Network For High Dynamic Range Imaging
• Authors: Tianhong Dai, Wei Li, Xilei Cao, Jianzhuang Liu, Xu Jia, Ales Leonardis, Youliang Yan, Shanxin Yuan
• Abstract summary: Existing methods, such as optical flow based and end-to-end deep learning based solutions, are error-prone either in detail restoration or ghosting artifacts removal. In this work, we propose a novel frequency-guided end-to-end deep neural network (FNet) to conduct HDR fusion in the frequency domain, and Wavelet Transform (DWT) is used to decompose inputs into different frequency bands. The low-frequency signals are used to avoid specific ghosting artifacts, while the high-frequency signals are used for preserving details.
• Score: 64.66969585951207
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High dynamic range (HDR) imaging from multiple low dynamic range (LDR) images has been suffering from ghosting artifacts caused by scene and objects motion. Existing methods, such as optical flow based and end-to-end deep learning based solutions, are error-prone either in detail restoration or ghosting artifacts removal. Comprehensive empirical evidence shows that ghosting artifacts caused by large foreground motion are mainly low-frequency signals and the details are mainly high-frequency signals. In this work, we propose a novel frequency-guided end-to-end deep neural network (FHDRNet) to conduct HDR fusion in the frequency domain, and Discrete Wavelet Transform (DWT) is used to decompose inputs into different frequency bands. The low-frequency signals are used to avoid specific ghosting artifacts, while the high-frequency signals are used for preserving details. Using a U-Net as the backbone, we propose two novel modules: merging module and frequency-guided upsampling module. The merging module applies the attention mechanism to the low-frequency components to deal with the ghost caused by large foreground motion. The frequency-guided upsampling module reconstructs details from multiple frequency-specific components with rich details. In addition, a new RAW dataset is created for training and evaluating multi-frame HDR imaging algorithms in the RAW domain. Extensive experiments are conducted on public datasets and our RAW dataset, showing that the proposed FHDRNet achieves state-of-the-art performance.

Related papers

• MFDNet: Multi-Frequency Deflare Network for Efficient Nighttime Flare Removal [39.70102431268123]
  We propose a lightweight Multi-Frequency Deflare Network (MFDNet) based on the Laplacian Pyramid. Our network decomposes the flare-corrupted image into low and high-frequency bands, effectively separating the illumination and content information in the image. Experimental results demonstrate that our approach outperforms state-of-the-art methods in removing nighttime flare on real-world and synthetic images.
  arXiv  Detail & Related papers  (2024-06-26T05:31:36Z)
• DenoDet: Attention as Deformable Multi-Subspace Feature Denoising for Target Detection in SAR Images [20.11145540094807]
  We propose a network aided by explicit frequency domain transform to calibrate convolutional biases and pay more attention to high-frequencies. We design TransDeno, a dynamic frequency domain attention module that performs as a transform domain soft thresholding operation. Our plug-and-play TransDeno sets state-of-the-art scores on multiple SAR target detection datasets.
  arXiv  Detail & Related papers  (2024-06-05T01:05:26Z)
• 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)
• Frequency-Aware Deepfake Detection: Improving Generalizability through Frequency Space Learning [81.98675881423131]
  This research addresses the challenge of developing a universal deepfake detector that can effectively identify unseen deepfake images. Existing frequency-based paradigms have relied on frequency-level artifacts introduced during the up-sampling in GAN pipelines to detect forgeries. We introduce a novel frequency-aware approach called FreqNet, centered around frequency domain learning, specifically designed to enhance the generalizability of deepfake detectors.
  arXiv  Detail & Related papers  (2024-03-12T01:28:00Z)
• 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)
• Multi-scale frequency separation network for image deblurring [10.511076996096117]
  We present a new method called multi-scale frequency separation network (MSFS-Net) for image deblurring. MSFS-Net captures the low and high-frequency information of image at multiple scales. Experiments on benchmark datasets show that the proposed network achieves state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-06-01T23:48:35Z)
• Exploring Inter-frequency Guidance of Image for Lightweight Gaussian Denoising [1.52292571922932]
  We propose a novel network architecture denoted as IGNet, in order to refine the frequency bands from low to high in a progressive manner. With this design, more inter-frequency prior and information are utilized, thus the model size can be lightened while still perserves competitive results.
  arXiv  Detail & Related papers  (2021-12-22T10:35:53Z)
• Deep Burst Super-Resolution [165.90445859851448]
  We propose a novel architecture for the burst super-resolution task. Our network takes multiple noisy RAW images as input, and generates a denoised, super-resolved RGB image as output. In order to enable training and evaluation on real-world data, we additionally introduce the BurstSR dataset.
  arXiv  Detail & Related papers  (2021-01-26T18:57:21Z)
• Wavelet Integrated CNNs for Noise-Robust Image Classification [51.18193090255933]
  We enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT) WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.
  arXiv  Detail & Related papers  (2020-05-07T09:10:41Z)

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.