Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing

• URL: http://arxiv.org/abs/2003.13912v1
• Date: Tue, 31 Mar 2020 02:07:33 GMT
• Title: Y-net: Multi-scale feature aggregation network with wavelet structure similarity loss function for single image dehazing
• Authors: Hao-Hsiang Yang, Chao-Han Huck Yang, Yi-Chang James Tsai
• Abstract summary: 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.
• Score: 18.479856828292935
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Single image dehazing is the ill-posed two-dimensional signal reconstruction problem. Recently, deep convolutional neural networks (CNN) have been successfully used in many computer vision problems. In this paper, we propose a Y-net that is named for its structure. This network reconstructs clear images by aggregating multi-scale features maps. Additionally, we propose a Wavelet Structure SIMilarity (W-SSIM) loss function in the training step. In the proposed loss function, discrete wavelet transforms are applied repeatedly to divide the image into differently sized patches with different frequencies and scales. The proposed loss function is the accumulation of SSIM loss of various patches with respective ratios. Extensive experimental results demonstrate that the proposed Y-net with the W-SSIM loss function restores high-quality clear images and outperforms state-of-the-art algorithms. Code and models are available at https://github.com/dectrfov/Y-net.

Related papers

• WiNet: Wavelet-based Incremental Learning for Efficient Medical Image Registration [68.25711405944239]
  Deep image registration has demonstrated exceptional accuracy and fast inference. Recent advances have adopted either multiple cascades or pyramid architectures to estimate dense deformation fields in a coarse-to-fine manner. We introduce a model-driven WiNet that incrementally estimates scale-wise wavelet coefficients for the displacement/velocity field across various scales.
  arXiv  Detail & Related papers  (2024-07-18T11:51:01Z)
• Parameter-Inverted Image Pyramid Networks [49.35689698870247]
  We propose a novel network architecture known as the Inverted Image Pyramid Networks (PIIP) Our core idea is to use models with different parameter sizes to process different resolution levels of the image pyramid. PIIP achieves superior performance in tasks such as object detection, segmentation, and image classification.
  arXiv  Detail & Related papers  (2024-06-06T17:59:10Z)
• Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurring [25.36888929483233]
  We propose a multi-scale network based on single-input and multiple-outputs(SIMO) for motion deblurring. We combine the characteristics of real-world trajectories with a learnable wavelet transform module to focus on the directional continuity and frequency features of the step-by-step transitions between blurred images to sharp images.
  arXiv  Detail & Related papers  (2023-12-29T02:59:40Z)
• 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)
• Riesz-Quincunx-UNet Variational Auto-Encoder for Satellite Image Denoising [0.0]
  We introduce a hybrid RQUNet-VAE scheme for image and time series decomposition used to reduce noise in satellite imagery. We also apply our scheme to several applications for multi-band satellite images, including: image denoising, image and time-series decomposition by diffusion and image segmentation.
  arXiv  Detail & Related papers  (2022-08-25T19:51:07Z)
• SiPRNet: End-to-End Learning for Single-Shot Phase Retrieval [8.820823270160695]
  convolutional neural networks (CNN) have played important roles in various image reconstruction tasks. In this paper, we design a novel CNN structure, named SiPRNet, to recover a signal from a single Fourier intensity measurement. The proposed approach consistently outperforms other CNN-based and traditional optimization-based methods in single-shot maskless phase retrieval.
  arXiv  Detail & Related papers  (2022-05-23T16:24:52Z)
• 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)
• Frequency Disentangled Residual Network [11.388328269522006]
  Residual networks (ResNets) have been utilized for various computer vision and image processing applications. A residual block consists of few convolutional layers having trainable parameters, which leads to overfitting. A frequency disentangled residual network (FDResNet) is proposed to tackle these issues.
  arXiv  Detail & Related papers  (2021-09-26T10:52:18Z)
• NeighCNN: A CNN based SAR Speckle Reduction using Feature preserving Loss Function [1.7188280334580193]
  NeighCNN is a deep learning-based speckle reduction algorithm that handles multiplicative noise. Various synthetic, as well as real SAR images, are used for testing the NeighCNN architecture.
  arXiv  Detail & Related papers  (2021-08-26T04:20:07Z)
• Asymmetric CNN for image super-resolution [102.96131810686231]
  Deep convolutional neural networks (CNNs) have been widely applied for low-level vision over the past five years. We propose an asymmetric CNN (ACNet) comprising an asymmetric block (AB), a mem?ory enhancement block (MEB) and a high-frequency feature enhancement block (HFFEB) for image super-resolution. Our ACNet can effectively address single image super-resolution (SISR), blind SISR and blind SISR of blind noise problems.
  arXiv  Detail & Related papers  (2021-03-25T07:10:46Z)
• Learning Frequency-aware Dynamic Network for Efficient Super-Resolution [56.98668484450857]
  This paper explores a novel frequency-aware dynamic network for dividing the input into multiple parts according to its coefficients in the discrete cosine transform (DCT) domain. In practice, the high-frequency part will be processed using expensive operations and the lower-frequency part is assigned with cheap operations to relieve the computation burden. Experiments conducted on benchmark SISR models and datasets show that the frequency-aware dynamic network can be employed for various SISR neural architectures.
  arXiv  Detail & Related papers  (2021-03-15T12:54:26Z)

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.