91 research outputs found
Non-Homogeneous Haze Removal via Artificial Scene Prior and Bidimensional Graph Reasoning
Due to the lack of natural scene and haze prior information, it is greatly
challenging to completely remove the haze from single image without distorting
its visual content. Fortunately, the real-world haze usually presents
non-homogeneous distribution, which provides us with many valuable clues in
partial well-preserved regions. In this paper, we propose a Non-Homogeneous
Haze Removal Network (NHRN) via artificial scene prior and bidimensional graph
reasoning. Firstly, we employ the gamma correction iteratively to simulate
artificial multiple shots under different exposure conditions, whose haze
degrees are different and enrich the underlying scene prior. Secondly, beyond
utilizing the local neighboring relationship, we build a bidimensional graph
reasoning module to conduct non-local filtering in the spatial and channel
dimensions of feature maps, which models their long-range dependency and
propagates the natural scene prior between the well-preserved nodes and the
nodes contaminated by haze. We evaluate our method on different benchmark
datasets. The results demonstrate that our method achieves superior performance
over many state-of-the-art algorithms for both the single image dehazing and
hazy image understanding tasks
ED-Dehaze Net: Encoder and Decoder Dehaze Network
The presence of haze will significantly reduce the quality of images, such as resulting in lower contrast and blurry details. This paper proposes a novel end-to-end dehazing method, called Encoder and Decoder Dehaze Network (ED-Dehaze Net), which contains a Generator and a Discriminator. In particular, the Generator uses an Encoder-Decoder structure to effectively extract the texture and semantic features of hazy images. Between the Encoder and Decoder we use Multi-Scale Convolution Block (MSCB) to enhance the process of feature extraction. The proposed ED-Dehaze Net is trained by combining Adversarial Loss, Perceptual Loss and Smooth L1 Loss. Quantitative and qualitative experimental results showed that our method can obtain the state-of-the-art dehazing performance
Breaking Through the Haze: An Advanced Non-Homogeneous Dehazing Method based on Fast Fourier Convolution and ConvNeXt
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.Comment: Accepted by CVPRW 202
Restoring Vision in Hazy Weather with Hierarchical Contrastive Learning
Image restoration under hazy weather condition, which is called single image
dehazing, has been of significant interest for various computer vision
applications. In recent years, deep learning-based methods have achieved
success. However, existing image dehazing methods typically neglect the
hierarchy of features in the neural network and fail to exploit their
relationships fully. To this end, we propose an effective image dehazing method
named Hierarchical Contrastive Dehazing (HCD), which is based on feature fusion
and contrastive learning strategies. HCD consists of a hierarchical dehazing
network (HDN) and a novel hierarchical contrastive loss (HCL). Specifically,
the core design in the HDN is a hierarchical interaction module, which utilizes
multi-scale activation to revise the feature responses hierarchically. To
cooperate with the training of HDN, we propose HCL which performs contrastive
learning on hierarchically paired exemplars, facilitating haze removal.
Extensive experiments on public datasets, RESIDE, HazeRD, and DENSE-HAZE,
demonstrate that HCD quantitatively outperforms the state-of-the-art methods in
terms of PSNR, SSIM and achieves better visual quality.Comment: 30 pages, 10 figure
GridFormer: Residual Dense Transformer with Grid Structure for Image Restoration in Adverse Weather Conditions
Image restoration in adverse weather conditions is a difficult task in
computer vision. In this paper, we propose a novel transformer-based framework
called GridFormer which serves as a backbone for image restoration under
adverse weather conditions. GridFormer is designed in a grid structure using a
residual dense transformer block, and it introduces two core designs. First, it
uses an enhanced attention mechanism in the transformer layer. The mechanism
includes stages of the sampler and compact self-attention to improve
efficiency, and a local enhancement stage to strengthen local information.
Second, we introduce a residual dense transformer block (RDTB) as the final
GridFormer layer. This design further improves the network's ability to learn
effective features from both preceding and current local features. The
GridFormer framework achieves state-of-the-art results on five diverse image
restoration tasks in adverse weather conditions, including image deraining,
dehazing, deraining & dehazing, desnowing, and multi-weather restoration. The
source code and pre-trained models will be released.Comment: 17 pages, 12 figure
Holistic Attention-Fusion Adversarial Network for Single Image Defogging
Adversarial learning-based image defogging methods have been extensively
studied in computer vision due to their remarkable performance. However, most
existing methods have limited defogging capabilities for real cases because
they are trained on the paired clear and synthesized foggy images of the same
scenes. In addition, they have limitations in preserving vivid color and rich
textual details in defogging. To address these issues, we develop a novel
generative adversarial network, called holistic attention-fusion adversarial
network (HAAN), for single image defogging. HAAN consists of a Fog2Fogfree
block and a Fogfree2Fog block. In each block, there are three learning-based
modules, namely, fog removal, color-texture recovery, and fog synthetic, that
are constrained each other to generate high quality images. HAAN is designed to
exploit the self-similarity of texture and structure information by learning
the holistic channel-spatial feature correlations between the foggy image with
its several derived images. Moreover, in the fog synthetic module, we utilize
the atmospheric scattering model to guide it to improve the generative quality
by focusing on an atmospheric light optimization with a novel sky segmentation
network. Extensive experiments on both synthetic and real-world datasets show
that HAAN outperforms state-of-the-art defogging methods in terms of
quantitative accuracy and subjective visual quality.Comment: 13 pages, 10 figure
Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing
Multi-stage architectures have exhibited efficacy in image dehazing, which
usually decomposes a challenging task into multiple more tractable sub-tasks
and progressively estimates latent hazy-free images. Despite the remarkable
progress, existing methods still suffer from the following shortcomings: (1)
limited exploration of frequency domain information; (2) insufficient
information interaction; (3) severe feature redundancy. To remedy these issues,
we propose a novel Mutual Information-driven Triple interaction Network
(MITNet) based on spatial-frequency dual domain information and two-stage
architecture. To be specific, the first stage, named amplitude-guided haze
removal, aims to recover the amplitude spectrum of the hazy images for haze
removal. And the second stage, named phase-guided structure refined, devotes to
learning the transformation and refinement of the phase spectrum. To facilitate
the information exchange between two stages, an Adaptive Triple Interaction
Module (ATIM) is developed to simultaneously aggregate cross-domain,
cross-scale, and cross-stage features, where the fused features are further
used to generate content-adaptive dynamic filters so that applying them to
enhance global context representation. In addition, we impose the mutual
information minimization constraint on paired scale encoder and decoder
features from both stages. Such an operation can effectively reduce information
redundancy and enhance cross-stage feature complementarity. Extensive
experiments on multiple public datasets exhibit that our MITNet performs
superior performance with lower model complexity.The code and models are
available at https://github.com/it-hao/MITNet.Comment: Accepted in ACM MM 202
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