105 research outputs found
Transmission Map and Atmospheric Light Guided Iterative Updater Network for Single Image Dehazing
Hazy images obscure content visibility and hinder several subsequent computer
vision tasks. For dehazing in a wide variety of hazy conditions, an end-to-end
deep network jointly estimating the dehazed image along with suitable
transmission map and atmospheric light for guidance could prove effective. To
this end, we propose an Iterative Prior Updated Dehazing Network (IPUDN) based
on a novel iterative update framework. We present a novel convolutional
architecture to estimate channel-wise atmospheric light, which along with an
estimated transmission map are used as priors for the dehazing network. Use of
channel-wise atmospheric light allows our network to handle color casts in hazy
images. In our IPUDN, the transmission map and atmospheric light estimates are
updated iteratively using corresponding novel updater networks. The iterative
mechanism is leveraged to gradually modify the estimates toward those
appropriately representing the hazy condition. These updates occur jointly with
the iterative estimation of the dehazed image using a convolutional neural
network with LSTM driven recurrence, which introduces inter-iteration
dependencies. Our approach is qualitatively and quantitatively found effective
for synthetic and real-world hazy images depicting varied hazy conditions, and
it outperforms the state-of-the-art. Thorough analyses of IPUDN through
additional experiments and detailed ablation studies are also presented.Comment: First two authors contributed equally. This work has been submitted
to the IEEE for possible publication. Copyright may be transferred without
notice, after which this version may no longer be accessible. Project
Website: https://aupendu.github.io/iterative-dehaz
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
Progressive Frequency-Aware Network for Laparoscopic Image Desmoking
Laparoscopic surgery offers minimally invasive procedures with better patient
outcomes, but smoke presence challenges visibility and safety. Existing
learning-based methods demand large datasets and high computational resources.
We propose the Progressive Frequency-Aware Network (PFAN), a lightweight GAN
framework for laparoscopic image desmoking, combining the strengths of CNN and
Transformer for progressive information extraction in the frequency domain.
PFAN features CNN-based Multi-scale Bottleneck-Inverting (MBI) Blocks for
capturing local high-frequency information and Locally-Enhanced Axial Attention
Transformers (LAT) for efficiently handling global low-frequency information.
PFAN efficiently desmokes laparoscopic images even with limited training data.
Our method outperforms state-of-the-art approaches in PSNR, SSIM, CIEDE2000,
and visual quality on the Cholec80 dataset and retains only 629K parameters.
Our code and models are made publicly available at:
https://github.com/jlzcode/PFAN
Non-aligned supervision for Real Image Dehazing
Removing haze from real-world images is challenging due to unpredictable
weather conditions, resulting in misaligned hazy and clear image pairs. In this
paper, we propose a non-aligned supervision framework that consists of three
networks - dehazing, airlight, and transmission. In particular, we explore a
non-alignment setting by utilizing a clear reference image that is not aligned
with the hazy input image to supervise the dehazing network through a
multi-scale reference loss that compares the features of the two images. Our
setting makes it easier to collect hazy/clear image pairs in real-world
environments, even under conditions of misalignment and shift views. To
demonstrate this, we have created a new hazy dataset called "Phone-Hazy", which
was captured using mobile phones in both rural and urban areas. Additionally,
we present a mean and variance self-attention network to model the infinite
airlight using dark channel prior as position guidance, and employ a channel
attention network to estimate the three-channel transmission. Experimental
results show that our framework outperforms current state-of-the-art methods in
the real-world image dehazing. Phone-Hazy and code will be available at
https://github.com/hello2377/NSDNet
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