56 research outputs found
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding
On the one hand, the dehazing task is an illposedness problem, which means
that no unique solution exists. On the other hand, the dehazing task should
take into account the subjective factor, which is to give the user selectable
dehazed images rather than a single result. Therefore, this paper proposes a
multi-output dehazing network by introducing illumination controllable ability,
called IC-Dehazing. The proposed IC-Dehazing can change the illumination
intensity by adjusting the factor of the illumination controllable module,
which is realized based on the interpretable Retinex theory. Moreover, the
backbone dehazing network of IC-Dehazing consists of a Transformer with double
decoders for high-quality image restoration. Further, the prior-based loss
function and unsupervised training strategy enable IC-Dehazing to complete the
parameter learning process without the need for paired data. To demonstrate the
effectiveness of the proposed IC-Dehazing, quantitative and qualitative
experiments are conducted on image dehazing, semantic segmentation, and object
detection tasks. Code is available at
https://github.com/Xiaofeng-life/ICDehazing
Learning Disentangled Feature Representation for Hybrid-distorted Image Restoration
Hybrid-distorted image restoration (HD-IR) is dedicated to restore real
distorted image that is degraded by multiple distortions. Existing HD-IR
approaches usually ignore the inherent interference among hybrid distortions
which compromises the restoration performance. To decompose such interference,
we introduce the concept of Disentangled Feature Learning to achieve the
feature-level divide-and-conquer of hybrid distortions. Specifically, we
propose the feature disentanglement module (FDM) to distribute feature
representations of different distortions into different channels by revising
gain-control-based normalization. We also propose a feature aggregation module
(FAM) with channel-wise attention to adaptively filter out the distortion
representations and aggregate useful content information from different
channels for the construction of raw image. The effectiveness of the proposed
scheme is verified by visualizing the correlation matrix of features and
channel responses of different distortions. Extensive experimental results also
prove superior performance of our approach compared with the latest HD-IR
schemes.Comment: Accepted by ECCV202
Model-based occlusion disentanglement for image-to-image translation
Image-to-image translation is affected by entanglement phenomena, which may
occur in case of target data encompassing occlusions such as raindrops, dirt,
etc. Our unsupervised model-based learning disentangles scene and occlusions,
while benefiting from an adversarial pipeline to regress physical parameters of
the occlusion model. The experiments demonstrate our method is able to handle
varying types of occlusions and generate highly realistic translations,
qualitatively and quantitatively outperforming the state-of-the-art on multiple
datasets.Comment: ECCV 202
Unsupervised Deraining: Where Asymmetric Contrastive Learning Meets Self-similarity
Most of the existing learning-based deraining methods are supervisedly
trained on synthetic rainy-clean pairs. The domain gap between the synthetic
and real rain makes them less generalized to complex real rainy scenes.
Moreover, the existing methods mainly utilize the property of the image or rain
layers independently, while few of them have considered their mutually
exclusive relationship. To solve above dilemma, we explore the intrinsic
intra-similarity within each layer and inter-exclusiveness between two layers
and propose an unsupervised non-local contrastive learning (NLCL) deraining
method. The non-local self-similarity image patches as the positives are
tightly pulled together, rain patches as the negatives are remarkably pushed
away, and vice versa. On one hand, the intrinsic self-similarity knowledge
within positive/negative samples of each layer benefits us to discover more
compact representation; on the other hand, the mutually exclusive property
between the two layers enriches the discriminative decomposition. Thus, the
internal self-similarity within each layer (similarity) and the external
exclusive relationship of the two layers (dissimilarity) serving as a generic
image prior jointly facilitate us to unsupervisedly differentiate the rain from
clean image. We further discover that the intrinsic dimension of the non-local
image patches is generally higher than that of the rain patches. This motivates
us to design an asymmetric contrastive loss to precisely model the compactness
discrepancy of the two layers for better discriminative decomposition. In
addition, considering that the existing real rain datasets are of low quality,
either small scale or downloaded from the internet, we collect a real
large-scale dataset under various rainy kinds of weather that contains
high-resolution rainy images.Comment: 16 pages, 15 figures. arXiv admin note: substantial text overlap with
arXiv:2203.1150
Rich Feature Distillation with Feature Affinity Module for Efficient Image Dehazing
Single-image haze removal is a long-standing hurdle for computer vision
applications. Several works have been focused on transferring advances from
image classification, detection, and segmentation to the niche of image
dehazing, primarily focusing on contrastive learning and knowledge
distillation. However, these approaches prove computationally expensive,
raising concern regarding their applicability to on-the-edge use-cases. This
work introduces a simple, lightweight, and efficient framework for single-image
haze removal, exploiting rich "dark-knowledge" information from a lightweight
pre-trained super-resolution model via the notion of heterogeneous knowledge
distillation. We designed a feature affinity module to maximize the flow of
rich feature semantics from the super-resolution teacher to the student
dehazing network. In order to evaluate the efficacy of our proposed framework,
its performance as a plug-and-play setup to a baseline model is examined. Our
experiments are carried out on the RESIDE-Standard dataset to demonstrate the
robustness of our framework to the synthetic and real-world domains. The
extensive qualitative and quantitative results provided establish the
effectiveness of the framework, achieving gains of upto 15\% (PSNR) while
reducing the model size by 20 times.Comment: Preprint version. Accepted at Opti
Enhancing Visibility in Nighttime Haze Images Using Guided APSF and Gradient Adaptive Convolution
Visibility in hazy nighttime scenes is frequently reduced by multiple
factors, including low light, intense glow, light scattering, and the presence
of multicolored light sources. Existing nighttime dehazing methods often
struggle with handling glow or low-light conditions, resulting in either
excessively dark visuals or unsuppressed glow outputs. In this paper, we
enhance the visibility from a single nighttime haze image by suppressing glow
and enhancing low-light regions. To handle glow effects, our framework learns
from the rendered glow pairs. Specifically, a light source aware network is
proposed to detect light sources of night images, followed by the APSF (Angular
Point Spread Function)-guided glow rendering. Our framework is then trained on
the rendered images, resulting in glow suppression. Moreover, we utilize
gradient-adaptive convolution, to capture edges and textures in hazy scenes. By
leveraging extracted edges and textures, we enhance the contrast of the scene
without losing important structural details. To boost low-light intensity, our
network learns an attention map, then adjusted by gamma correction. This
attention has high values on low-light regions and low values on haze and glow
regions. Extensive evaluation on real nighttime haze images, demonstrates the
effectiveness of our method. Our experiments demonstrate that our method
achieves a PSNR of 30.38dB, outperforming state-of-the-art methods by 13 on
GTA5 nighttime haze dataset. Our data and code is available at:
\url{https://github.com/jinyeying/nighttime_dehaze}.Comment: Accepted to ACM'MM2023, https://github.com/jinyeying/nighttime_dehaz
HybrUR: A Hybrid Physical-Neural Solution for Unsupervised Underwater Image Restoration
Robust vision restoration for an underwater image remains a challenging
problem. For the lack of aligned underwater-terrestrial image pairs, the
unsupervised method is more suited to this task. However, the pure data-driven
unsupervised method usually has difficulty in achieving realistic color
correction for lack of optical constraint. In this paper, we propose a data-
and physics-driven unsupervised architecture that learns underwater vision
restoration from unpaired underwater-terrestrial images. For sufficient domain
transformation and detail preservation, the underwater degeneration needs to be
explicitly constructed based on the optically unambiguous physics law. Thus, we
employ the Jaffe-McGlamery degradation theory to design the generation models,
and use neural networks to describe the process of underwater degradation.
Furthermore, to overcome the problem of invalid gradient when optimizing the
hybrid physical-neural model, we fully investigate the intrinsic correlation
between the scene depth and the degradation factors for the backscattering
estimation, to improve the restoration performance through physical
constraints. Our experimental results show that the proposed method is able to
perform high-quality restoration for unconstrained underwater images without
any supervision. On multiple benchmarks, we outperform several state-of-the-art
supervised and unsupervised approaches. We also demonstrate that our methods
yield encouraging results on real-world applications
An Enhancement in Single-Image Dehazing Employing Contrastive Attention over Variational Auto-Encoder (CA-VAE) Method
Hazy images and videos have low contrast and poor visibility. Fog, ice fog, steam fog, smoke, volcanic ash, dust, and snow are all terrible conditions for capturing images and worsening color and contrast. Computer vision applications often fail due to image degradation. Hazy images and videos with skewed color contrasts and low visibility affect photometric analysis, object identification, and target tracking. Computer programs can classify and comprehend images using image haze reduction algorithms. Image dehazing now uses deep learning approaches. The observed negative correlation between depth and the difference between the hazy image’s maximum and lowest color channels inspired the suggested study. Using a contrasting attention mechanism spanning sub-pixels and blocks, we offer a unique attention method to create high-quality, haze-free pictures. The L*a*b* color model has been proposed as an effective color space for dehazing images. A variational auto-encoder-based dehazing network may also be utilized for training since it compresses and attempts to reconstruct input images. Estimating hundreds of image-impacting characteristics may be necessary. In a variational auto-encoder, fuzzy input images are directly given a Gaussian probability distribution, and the variational auto-encoder estimates the distribution parameters. A quantitative and qualitative study of the RESIDE dataset will show the suggested method's accuracy and resilience. RESIDE’s subsets of synthetic and real-world single-image dehazing examples are utilized for training and assessment. Enhance the structural similarity index measure (SSIM) and peak signal-to-noise ratio metrics (PSNR)
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