97 research outputs found
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
Uni-Removal: A Semi-Supervised Framework for Simultaneously Addressing Multiple Degradations in Real-World Images
Removing multiple degradations, such as haze, rain, and blur, from real-world
images poses a challenging and illposed problem. Recently, unified models that
can handle different degradations have been proposed and yield promising
results. However, these approaches focus on synthetic images and experience a
significant performance drop when applied to realworld images. In this paper,
we introduce Uni-Removal, a twostage semi-supervised framework for addressing
the removal of multiple degradations in real-world images using a unified model
and parameters. In the knowledge transfer stage, Uni-Removal leverages a
supervised multi-teacher and student architecture in the knowledge transfer
stage to facilitate learning from pretrained teacher networks specialized in
different degradation types. A multi-grained contrastive loss is introduced to
enhance learning from feature and image spaces. In the domain adaptation stage,
unsupervised fine-tuning is performed by incorporating an adversarial
discriminator on real-world images. The integration of an extended
multi-grained contrastive loss and generative adversarial loss enables the
adaptation of the student network from synthetic to real-world domains.
Extensive experiments on real-world degraded datasets demonstrate the
effectiveness of our proposed method. We compare our Uni-Removal framework with
state-of-the-art supervised and unsupervised methods, showcasing its promising
results in real-world image dehazing, deraining, and deblurring simultaneously
NDELS: A Novel Approach for Nighttime Dehazing, Low-Light Enhancement, and Light Suppression
This paper tackles the intricate challenge of improving the quality of
nighttime images under hazy and low-light conditions. Overcoming issues
including nonuniform illumination glows, texture blurring, glow effects, color
distortion, noise disturbance, and overall, low light have proven daunting.
Despite the inherent difficulties, this paper introduces a pioneering solution
named Nighttime Dehazing, Low-Light Enhancement, and Light Suppression (NDELS).
NDELS utilizes a unique network that combines three essential processes to
enhance visibility, brighten low-light regions, and effectively suppress glare
from bright light sources. In contrast to limited progress in nighttime
dehazing, unlike its daytime counterpart, NDELS presents a comprehensive and
innovative approach. The efficacy of NDELS is rigorously validated through
extensive comparisons with eight state-of-the-art algorithms across four
diverse datasets. Experimental results showcase the superior performance of our
method, demonstrating its outperformance in terms of overall image quality,
including color and edge enhancement. Quantitative (PSNR, SSIM) and qualitative
metrics (CLIPIQA, MANIQA, TRES), measure these results
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
A Non-Reference Evaluation of Underwater Image Enhancement Methods Using a New Underwater Image Dataset
The rise of vision-based environmental, marine, and oceanic exploration research highlights the need for supporting underwater image enhancement techniques to help mitigate water effects on images such as blurriness, low color contrast, and poor quality. This paper presents an evaluation of common underwater image enhancement techniques using a new underwater image dataset. The collected dataset is comprised of 100 images of aquatic plants taken at a shallow depth of up to three meters from three different locations in the Great Lake Superior, USA, via a Remotely Operated Vehicle (ROV) equipped with a high-definition RGB camera. In particular, we use our dataset to benchmark nine state-of-the-art image enhancement models at three different depths using a set of common non-reference image quality evaluation metrics. Then we provide a comparative analysis of the performance of the selected models at different depths and highlight the most prevalent ones. The obtained results show that the selected image enhancement models are capable of producing considerably better-quality images with some models performing better than others at certain depths
Removing Image Artifacts From Scratched Lens Protectors
A protector is placed in front of the camera lens for mobile devices to avoid
damage, while the protector itself can be easily scratched accidentally,
especially for plastic ones. The artifacts appear in a wide variety of
patterns, making it difficult to see through them clearly. Removing image
artifacts from the scratched lens protector is inherently challenging due to
the occasional flare artifacts and the co-occurring interference within mixed
artifacts. Though different methods have been proposed for some specific
distortions, they seldom consider such inherent challenges. In our work, we
consider the inherent challenges in a unified framework with two cooperative
modules, which facilitate the performance boost of each other. We also collect
a new dataset from the real world to facilitate training and evaluation
purposes. The experimental results demonstrate that our method outperforms the
baselines qualitatively and quantitatively. The code and datasets will be
released after acceptance.Comment: Accepted by ISCAS 202
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