Dense U-net for super-resolution with shuffle pooling layer

• URL: http://arxiv.org/abs/2011.05490v2
• Date: Sat, 9 Jan 2021 05:58:08 GMT
• Title: Dense U-net for super-resolution with shuffle pooling layer
• Authors: Zhengyang Lu and Ying Chen
• Abstract summary: Recent researches have achieved great progress on single image super-resolution(SISR) In these method, the high resolution input image is down-scaled to low resolution space using a single filter, commonly max-pooling, before feature extraction. We demonstrate that this is sub-optimal and causes information loss. In this work, we proposed a state-of-the-art convolutional neural network method called Dense U-net with shuffle pooling.
• Score: 4.397981844057195
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent researches have achieved great progress on single image super-resolution(SISR) due to the development of deep learning in the field of computer vision. In these method, the high resolution input image is down-scaled to low resolution space using a single filter, commonly max-pooling, before feature extraction. This means that the feature extraction is performed in biased filtered feature space. We demonstrate that this is sub-optimal and causes information loss. In this work, we proposed a state-of-the-art convolutional neural network method called Dense U-net with shuffle pooling. To achieve this, a modified U-net with dense blocks, called dense U-net, is proposed for SISR. Then, a new pooling strategy called shuffle pooling is designed, which is aimed to replace the dense U-Net for down-scale operation. By doing so, we effectively replace the handcrafted filter in the SISR pipeline with more lossy down-sampling filters specifically trained for each feature map, whilst also reducing the information loss of the overall SISR operation. In addition, a mix loss function, which combined with Mean Square Error(MSE), Structural Similarity Index(SSIM) and Mean Gradient Error (MGE), comes up to reduce the perception loss and high-level information loss. Our proposed method achieves superior accuracy over previous state-of-the-art on the three benchmark datasets: SET14, BSD300, ICDAR2003. Code is available online.

Related papers

• Reducing Redundancy in the Bottleneck Representation of the Autoencoders [98.78384185493624]
  Autoencoders are a type of unsupervised neural networks, which can be used to solve various tasks. We propose a scheme to explicitly penalize feature redundancies in the bottleneck representation. We tested our approach across different tasks: dimensionality reduction using three different dataset, image compression using the MNIST dataset, and image denoising using fashion MNIST.
  arXiv  Detail & Related papers  (2022-02-09T18:48:02Z)
• The KFIoU Loss for Rotated Object Detection [115.334070064346]
  In this paper, we argue that one effective alternative is to devise an approximate loss who can achieve trend-level alignment with SkewIoU loss. Specifically, we model the objects as Gaussian distribution and adopt Kalman filter to inherently mimic the mechanism of SkewIoU. The resulting new loss called KFIoU is easier to implement and works better compared with exact SkewIoU.
  arXiv  Detail & Related papers  (2022-01-29T10:54:57Z)
• Deep Amended Gradient Descent for Efficient Spectral Reconstruction from Single RGB Images [42.26124628784883]
  We propose a compact, efficient, and end-to-end learning-based framework, namely AGD-Net. We first formulate the problem explicitly based on the classic gradient descent algorithm. AGD-Net can improve the reconstruction quality by more than 1.0 dB on average.
  arXiv  Detail & Related papers  (2021-08-12T05:54:09Z)
• PLADE-Net: Towards Pixel-Level Accuracy for Self-Supervised Single-View Depth Estimation with Neural Positional Encoding and Distilled Matting Loss [49.66736599668501]
  We propose a self-supervised single-view pixel-level accurate depth estimation network, called PLADE-Net. Our method shows unprecedented accuracy levels, exceeding 95% in terms of the $delta1$ metric on the KITTI dataset.
  arXiv  Detail & Related papers  (2021-03-12T15:54:46Z)
• Unrolling of Deep Graph Total Variation for Image Denoising [106.93258903150702]
  In this paper, we combine classical graph signal filtering with deep feature learning into a competitive hybrid design. We employ interpretable analytical low-pass graph filters and employ 80% fewer network parameters than state-of-the-art DL denoising scheme DnCNN.
  arXiv  Detail & Related papers  (2020-10-21T20:04:22Z)
• LiteDepthwiseNet: An Extreme Lightweight Network for Hyperspectral Image Classification [9.571458051525768]
  This paper proposes a new network architecture, LiteDepthwiseNet, for hyperspectral image (HSI) classification. LiteDepthwiseNet decomposes standard convolution into depthwise convolution and pointwise convolution, which can achieve high classification performance with minimal parameters. Experiment results on three benchmark hyperspectral datasets show that LiteDepthwiseNet achieves state-of-the-art performance with a very small number of parameters and low computational cost.
  arXiv  Detail & Related papers  (2020-10-15T13:12:17Z)
• Monocular Depth Estimation Using Multi Scale Neural Network And Feature Fusion [0.0]
  Our network uses two different blocks, first which uses different filter sizes for convolution and merges all the individual feature maps. The second block uses dilated convolutions in place of fully connected layers thus reducing computations and increasing the receptive field. We train and test our network on Make 3D dataset, NYU Depth V2 dataset and Kitti dataset using standard evaluation metrics for depth estimation comprised of RMSE loss and SILog loss.
  arXiv  Detail & Related papers  (2020-09-11T18:08:52Z)
• Progressively Guided Alternate Refinement Network for RGB-D Salient Object Detection [63.18846475183332]
  We aim to develop an efficient and compact deep network for RGB-D salient object detection. We propose a progressively guided alternate refinement network to refine it. Our model outperforms existing state-of-the-art approaches by a large margin.
  arXiv  Detail & Related papers  (2020-08-17T02:55:06Z)
• Hierarchical Dynamic Filtering Network for RGB-D Salient Object Detection [91.43066633305662]
  The main purpose of RGB-D salient object detection (SOD) is how to better integrate and utilize cross-modal fusion information. In this paper, we explore these issues from a new perspective. We implement a kind of more flexible and efficient multi-scale cross-modal feature processing.
  arXiv  Detail & Related papers  (2020-07-13T07:59:55Z)
• Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing [18.479856828292935]
  We propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating multi-scale features maps. We also propose a Wavelet Structure SIMilarity (W-SSIM) loss function in the training step.
  arXiv  Detail & Related papers  (2020-03-31T02:07:33Z)

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.