Multi-Scale Adaptive Network for Single Image Denoising

• URL: http://arxiv.org/abs/2203.04313v1
• Date: Tue, 8 Mar 2022 15:13:20 GMT
• Title: Multi-Scale Adaptive Network for Single Image Denoising
• Authors: Yuanbiao Gou, Peng Hu, Jiancheng Lv, Xi Peng
• Abstract summary: We propose a novel Multi-Scale Adaptive Network (MSANet) for single image denoising. MSANet simultaneously embraces the within-scale characteristics and the cross-scale complementarity. Experiments on both three real and six synthetic noisy image datasets show the superiority of MSANet compared with 12 methods.
• Score: 28.54807194038972
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Multi-scale architectures have shown effectiveness in a variety of tasks including single image denoising, thanks to appealing cross-scale complementarity. However, existing methods treat different scale features equally without considering their scale-specific characteristics, i.e., the within-scale characteristics are ignored. In this paper, we reveal this missing piece for multi-scale architecture design and accordingly propose a novel Multi-Scale Adaptive Network (MSANet) for single image denoising. To be specific, MSANet simultaneously embraces the within-scale characteristics and the cross-scale complementarity thanks to three novel neural blocks, i.e., adaptive feature block (AFeB), adaptive multi-scale block (AMB), and adaptive fusion block (AFuB). In brief, AFeB is designed to adaptively select details and filter noises, which is highly expected for fine-grained features. AMB could enlarge the receptive field and aggregate the multi-scale information, which is designed to satisfy the demands of both fine- and coarse-grained features. AFuB devotes to adaptively sampling and transferring the features from one scale to another scale, which is used to fuse the features with varying characteristics from coarse to fine. Extensive experiments on both three real and six synthetic noisy image datasets show the superiority of MSANet compared with 12 methods.

Related papers

• Robust Network Learning via Inverse Scale Variational Sparsification [55.64935887249435]
  We introduce an inverse scale variational sparsification framework within a time-continuous inverse scale space formulation. Unlike frequency-based methods, our approach not only removes noise by smoothing small-scale features. We show the efficacy of our approach through enhanced robustness against various noise types.
  arXiv  Detail & Related papers  (2024-09-27T03:17:35Z)
• Towards Self-Supervised FG-SBIR with Unified Sample Feature Alignment and Multi-Scale Token Recycling [11.129453244307369]
  FG-SBIR aims to minimize the distance between sketches and corresponding images in the embedding space. We propose an effective approach to narrow the gap between the two domains. It mainly facilitates unified mutual information sharing both intra- and inter-samples.
  arXiv  Detail & Related papers  (2024-06-17T13:49:12Z)
• Multi-scale Unified Network for Image Classification [33.560003528712414]
  CNNs face notable challenges in performance and computational efficiency when dealing with real-world, multi-scale image inputs. We propose Multi-scale Unified Network (MUSN) consisting of multi-scales, a unified network, and scale-invariant constraint. MUSN yields an accuracy increase up to 44.53% and diminishes FLOPs by 7.01-16.13% in multi-scale scenarios.
  arXiv  Detail & Related papers  (2024-03-27T06:40:26Z)
• Rotated Multi-Scale Interaction Network for Referring Remote Sensing Image Segmentation [63.15257949821558]
  Referring Remote Sensing Image (RRSIS) is a new challenge that combines computer vision and natural language processing. Traditional Referring Image (RIS) approaches have been impeded by the complex spatial scales and orientations found in aerial imagery. We introduce the Rotated Multi-Scale Interaction Network (RMSIN), an innovative approach designed for the unique demands of RRSIS.
  arXiv  Detail & Related papers  (2023-12-19T08:14:14Z)
• Spatially-Adaptive Feature Modulation for Efficient Image Super-Resolution [90.16462805389943]
  We develop a spatially-adaptive feature modulation (SAFM) mechanism upon a vision transformer (ViT)-like block. Proposed method is $3times$ smaller than state-of-the-art efficient SR methods.
  arXiv  Detail & Related papers  (2023-02-27T14:19:31Z)
• Effective Invertible Arbitrary Image Rescaling [77.46732646918936]
  Invertible Neural Networks (INN) are able to increase upscaling accuracy significantly by optimizing the downscaling and upscaling cycle jointly. A simple and effective invertible arbitrary rescaling network (IARN) is proposed to achieve arbitrary image rescaling by training only one model in this work. It is shown to achieve a state-of-the-art (SOTA) performance in bidirectional arbitrary rescaling without compromising perceptual quality in LR outputs.
  arXiv  Detail & Related papers  (2022-09-26T22:22:30Z)
• BIMS-PU: Bi-Directional and Multi-Scale Point Cloud Upsampling [60.257912103351394]
  We develop a new point cloud upsampling pipeline called BIMS-PU. We decompose the up/downsampling procedure into several up/downsampling sub-steps by breaking the target sampling factor into smaller factors. We show that our method achieves superior results to state-of-the-art approaches.
  arXiv  Detail & Related papers  (2022-06-25T13:13:37Z)
• Progressive Multi-scale Consistent Network for Multi-class Fundus Lesion Segmentation [28.58972084293778]
  We propose a progressive multi-scale consistent network (PMCNet) that integrates the proposed progressive feature fusion (PFF) block and dynamic attention block (DAB) PFF block progressively integrates multi-scale features from adjacent encoding layers, facilitating feature learning of each layer by aggregating fine-grained details and high-level semantics. DAB is designed to dynamically learn the attentive cues from the fused features at different scales, thus aiming to smooth the essential conflicts existing in multi-scale features.
  arXiv  Detail & Related papers  (2022-05-31T12:10:01Z)
• Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing [31.543771270803056]
  In this paper, we pay attention to the feature extraction and utilization of the input image itself. We propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales. Meanwhile, we design a Multi-scale Feature Fusion Module (MFFM) and an Adaptive Feature Selection Module (AFSM) to achieve the selection and fusion of features at different scales.
  arXiv  Detail & Related papers  (2021-02-24T08:53:14Z)
• MDCN: Multi-scale Dense Cross Network for Image Super-Resolution [35.59973281360541]
  We propose a Multi-scale Dense Cross Network (MDCN), which achieves great performance with fewer parameters and less execution time. MDCN consists of multi-scale dense cross blocks (MDCBs), hierarchical feature distillation block (HFDB), and dynamic reconstruction block (DRB)
  arXiv  Detail & Related papers  (2020-08-30T03:50:19Z)
• Sequential Hierarchical Learning with Distribution Transformation for Image Super-Resolution [83.70890515772456]
  We build a sequential hierarchical learning super-resolution network (SHSR) for effective image SR. We consider the inter-scale correlations of features, and devise a sequential multi-scale block (SMB) to progressively explore the hierarchical information. Experiment results show SHSR achieves superior quantitative performance and visual quality to state-of-the-art methods.
  arXiv  Detail & Related papers  (2020-07-19T01:35:53Z)

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.