AADNet: Attention aware Demoiréing Network

• URL: http://arxiv.org/abs/2403.08384v2
• Date: Mon, 6 May 2024 04:14:15 GMT
• Title: AADNet: Attention aware Demoiréing Network
• Authors: M Rakesh Reddy, Shubham Mandloi, Aman Kumar,
• Abstract summary: Moire pattern frequently appears in photographs captured with mobile devices and digital cameras. We propose a novel lightweight architecture, AADNet, for high-resolution image demoire'ing.
• Score: 2.1626093085892144
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Moire pattern frequently appears in photographs captured with mobile devices and digital cameras, potentially degrading image quality. Despite recent advancements in computer vision, image demoire'ing remains a challenging task due to the dynamic textures and variations in colour, shape, and frequency of moire patterns. Most existing methods struggle to generalize to unseen datasets, limiting their effectiveness in removing moire patterns from real-world scenarios. In this paper, we propose a novel lightweight architecture, AADNet (Attention Aware Demoireing Network), for high-resolution image demoire'ing that effectively works across different frequency bands and generalizes well to unseen datasets. Extensive experiments conducted on the UHDM dataset validate the effectiveness of our approach, resulting in high-fidelity images.

Related papers

• Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data Generation [53.95204595640208]
  Data-Free Knowledge Distillation (DFKD) is an advanced technique that enables knowledge transfer from a teacher model to a student model without relying on original training data. Previous approaches have generated synthetic images at high resolutions without leveraging information from real images. MUSE generates images at lower resolutions while using Class Activation Maps (CAMs) to ensure that the generated images retain critical, class-specific features.
  arXiv  Detail & Related papers  (2024-11-26T02:23:31Z)
• TSNet:A Two-stage Network for Image Dehazing with Multi-scale Fusion and Adaptive Learning [0.37240490024629924]
  This paper proposes a two-stage image dehazing network called TSNet. It consists of the multi-scale fusion module (MSFM) and the adaptive learning module (ALM) Extensive experiments demonstrate that TSNet exhibits superior dehazing performance on both synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2024-04-03T05:02:46Z)
• DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement [77.0360085530701]
  Underwater image enhancement (UIE) is a challenging task due to the complex degradation caused by underwater environments. Previous methods often idealize the degradation process, and neglect the impact of medium noise and object motion on the distribution of image features. Our approach utilizes predicted images to dynamically update pseudo-labels, adding a dynamic gradient to optimize the network's gradient space.
  arXiv  Detail & Related papers  (2023-12-12T06:07:21Z)
• An Ensemble Model for Distorted Images in Real Scenarios [0.0]
  In this paper, we apply the object detector YOLOv7 to detect distorted images from the CDCOCO dataset. Through carefully designed optimizations, our model achieves excellent performance on the CDCOCO test set. Our denoising detection model can denoise and repair distorted images, making the model useful in a variety of real-world scenarios and environments.
  arXiv  Detail & Related papers  (2023-09-26T15:12:55Z)
• FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment [6.507353572917133]
  We propose a novel model called FPANet that learns filters in both frequency and spatial domains. We demonstrate the effectiveness of our proposed method with a publicly available large-scale dataset.
  arXiv  Detail & Related papers  (2023-01-18T06:37:24Z)
• Towards Efficient and Scale-Robust Ultra-High-Definition Image Demoireing [71.62289021118983]
  We present an efficient baseline model ESDNet for tackling 4K moire images, wherein we build a semantic-aligned scale-aware module to address the scale variation of moire patterns. Our approach outperforms state-of-the-art methods by a large margin while being much more lightweight.
  arXiv  Detail & Related papers  (2022-07-20T14:20:52Z)
• High-Frequency aware Perceptual Image Enhancement [0.08460698440162888]
  We introduce a novel deep neural network suitable for multi-scale analysis and propose efficient model-agnostic methods. Our model can be applied to multi-scale image enhancement problems including denoising, deblurring and single image super-resolution.
  arXiv  Detail & Related papers  (2021-05-25T07:33:14Z)
• Enhancing Photorealism Enhancement [83.88433283714461]
  We present an approach to enhancing the realism of synthetic images using a convolutional network. We analyze scene layout distributions in commonly used datasets and find that they differ in important ways. We report substantial gains in stability and realism in comparison to recent image-to-image translation methods.
  arXiv  Detail & Related papers  (2021-05-10T19:00:49Z)
• Improving robustness against common corruptions with frequency biased models [112.65717928060195]
  unseen image corruptions can cause a surprisingly large drop in performance. Image corruption types have different characteristics in the frequency spectrum and would benefit from a targeted type of data augmentation. We propose a new regularization scheme that minimizes the total variation (TV) of convolution feature-maps to increase high-frequency robustness.
  arXiv  Detail & Related papers  (2021-03-30T10:44:50Z)
• Wavelet-Based Dual-Branch Network for Image Demoireing [148.91145614517015]
  We design a wavelet-based dual-branch network (WDNet) with a spatial attention mechanism for image demoireing. Our network removes moire patterns in the wavelet domain to separate the frequencies of moire patterns from the image content. Experiments demonstrate the effectiveness of our method, and we further show that WDNet generalizes to removing moire artifacts on non-screen images.
  arXiv  Detail & Related papers  (2020-07-14T16:44:30Z)

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.