Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt

• URL: http://arxiv.org/abs/2305.04430v1
• Date: Mon, 8 May 2023 02:59:02 GMT
• Title: Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt
• Authors: Han Zhou, Wei Dong, Yangyi Liu and Jun Chen
• Abstract summary: Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained ConvNeXt model. Our model is able to effectively explore global contextual information and produce images with better perceptual quality.
• Score: 14.917290578644424
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Haze usually leads to deteriorated images with low contrast, color shift and structural distortion. We observe that many deep learning based models exhibit exceptional performance on removing homogeneous haze, but they usually fail to address the challenge of non-homogeneous dehazing. Two main factors account for this situation. Firstly, due to the intricate and non uniform distribution of dense haze, the recovery of structural and chromatic features with high fidelity is challenging, particularly in regions with heavy haze. Secondly, the existing small scale datasets for non-homogeneous dehazing are inadequate to support reliable learning of feature mappings between hazy images and their corresponding haze-free counterparts by convolutional neural network (CNN)-based models. To tackle these two challenges, we propose a novel two branch network that leverages 2D discrete wavelete transform (DWT), fast Fourier convolution (FFC) residual block and a pretrained ConvNeXt model. Specifically, in the DWT-FFC frequency branch, our model exploits DWT to capture more high-frequency features. Moreover, by taking advantage of the large receptive field provided by FFC residual blocks, our model is able to effectively explore global contextual information and produce images with better perceptual quality. In the prior knowledge branch, an ImageNet pretrained ConvNeXt as opposed to Res2Net is adopted. This enables our model to learn more supplementary information and acquire a stronger generalization ability. The feasibility and effectiveness of the proposed method is demonstrated via extensive experiments and ablation studies. The code is available at https://github.com/zhouh115/DWT-FFC.

Related papers

• LinFusion: 1 GPU, 1 Minute, 16K Image [71.44735417472043]
  We introduce a low-rank approximation of a wide spectrum of popular linear token mixers. We find that the distilled model, termed LinFusion, achieves performance on par with or superior to the original SD. Experiments on SD-v1.5, SD-v2.1, and SD-XL demonstrate that LinFusion enables satisfactory and efficient zero-shot cross-resolution generation.
  arXiv  Detail & Related papers  (2024-09-03T17:54:39Z)
• Realistic Extreme Image Rescaling via Generative Latent Space Learning [51.85790402171696]
  We propose a novel framework called Latent Space Based Image Rescaling (LSBIR) for extreme image rescaling tasks. LSBIR effectively leverages powerful natural image priors learned by a pre-trained text-to-image diffusion model to generate realistic HR images. In the first stage, a pseudo-invertible encoder-decoder models the bidirectional mapping between the latent features of the HR image and the target-sized LR image. In the second stage, the reconstructed features from the first stage are refined by a pre-trained diffusion model to generate more faithful and visually pleasing details.
  arXiv  Detail & Related papers  (2024-08-17T09:51:42Z)
• CFG++: Manifold-constrained Classifier Free Guidance for Diffusion Models [52.29804282879437]
  CFG++ is a novel approach that tackles the offmanifold challenges inherent to traditional CFG. It offers better inversion-to-image generation, invertibility, smaller guidance scales, reduced mode collapse, etc. It can be easily integrated into high-order diffusion solvers and naturally extends to distilled diffusion models.
  arXiv  Detail & Related papers  (2024-06-12T10:40:10Z)
• DiAD: A Diffusion-based Framework for Multi-class Anomaly Detection [55.48770333927732]
  We propose a Difusion-based Anomaly Detection (DiAD) framework for multi-class anomaly detection. It consists of a pixel-space autoencoder, a latent-space Semantic-Guided (SG) network with a connection to the stable diffusion's denoising network, and a feature-space pre-trained feature extractor. Experiments on MVTec-AD and VisA datasets demonstrate the effectiveness of our approach.
  arXiv  Detail & Related papers  (2023-12-11T18:38:28Z)
• Distance Weighted Trans Network for Image Completion [52.318730994423106]
  We propose a new architecture that relies on Distance-based Weighted Transformer (DWT) to better understand the relationships between an image's components. CNNs are used to augment the local texture information of coarse priors. DWT blocks are used to recover certain coarse textures and coherent visual structures.
  arXiv  Detail & Related papers  (2023-10-11T12:46:11Z)
• Frequency Compensated Diffusion Model for Real-scene Dehazing [6.105813272271171]
  We consider a dehazing framework based on conditional diffusion models for improved generalization to real haze. The proposed dehazing diffusion model significantly outperforms state-of-the-art methods on real-world images.
  arXiv  Detail & Related papers  (2023-08-21T06:50:44Z)
• DiffDis: Empowering Generative Diffusion Model with Cross-Modal Discrimination Capability [75.9781362556431]
  We propose DiffDis to unify the cross-modal generative and discriminative pretraining into one single framework under the diffusion process. We show that DiffDis outperforms single-task models on both the image generation and the image-text discriminative tasks.
  arXiv  Detail & Related papers  (2023-08-18T05:03:48Z)
• Learning A Coarse-to-Fine Diffusion Transformer for Image Restoration [39.071637725773314]
  We propose a coarse-to-fine diffusion Transformer (C2F-DFT) for image restoration. C2F-DFT contains diffusion self-attention (DFSA) and diffusion feed-forward network (DFN) In the coarse training stage, our C2F-DFT estimates noises and then generates the final clean image by a sampling algorithm.
  arXiv  Detail & Related papers  (2023-08-17T01:59:59Z)
• Self-Regression Learning for Blind Hyperspectral Image Fusion Without Label [11.291055330647977]
  We propose a self-regression learning method that reconstructs hyperspectral image (HSI) and estimate the observation model. In particular, we adopt an invertible neural network (INN) for restoring the HSI, and two fully-connected networks (FCN) for estimating the observation model. Our model can outperform the state-of-the-art methods in experiments on both synthetic and real-world dataset.
  arXiv  Detail & Related papers  (2021-03-31T04:48:21Z)
• A GAN-Based Input-Size Flexibility Model for Single Image Dehazing [16.83211957781034]
  This paper concentrates on the challenging task of single image dehazing. We design a novel model to directly generate the haze-free image. Considering this reason and various image sizes, we propose a novel input-size flexibility conditional generative adversarial network (cGAN) for single image dehazing.
  arXiv  Detail & Related papers  (2021-02-19T08:27:17Z)

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.