1,247 research outputs found
DiffLLE: Diffusion-guided Domain Calibration for Unsupervised Low-light Image Enhancement
Existing unsupervised low-light image enhancement methods lack enough
effectiveness and generalization in practical applications. We suppose this is
because of the absence of explicit supervision and the inherent gap between
real-world scenarios and the training data domain. In this paper, we develop
Diffusion-based domain calibration to realize more robust and effective
unsupervised Low-Light Enhancement, called DiffLLE. Since the diffusion model
performs impressive denoising capability and has been trained on massive clean
images, we adopt it to bridge the gap between the real low-light domain and
training degradation domain, while providing efficient priors of real-world
content for unsupervised models. Specifically, we adopt a naive unsupervised
enhancement algorithm to realize preliminary restoration and design two
zero-shot plug-and-play modules based on diffusion model to improve
generalization and effectiveness. The Diffusion-guided Degradation Calibration
(DDC) module narrows the gap between real-world and training low-light
degradation through diffusion-based domain calibration and a lightness
enhancement curve, which makes the enhancement model perform robustly even in
sophisticated wild degradation. Due to the limited enhancement effect of the
unsupervised model, we further develop the Fine-grained Target domain
Distillation (FTD) module to find a more visual-friendly solution space. It
exploits the priors of the pre-trained diffusion model to generate
pseudo-references, which shrinks the preliminary restored results from a coarse
normal-light domain to a finer high-quality clean field, addressing the lack of
strong explicit supervision for unsupervised methods. Benefiting from these,
our approach even outperforms some supervised methods by using only a simple
unsupervised baseline. Extensive experiments demonstrate the superior
effectiveness of the proposed DiffLLE
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
Low-Light Image and Video Enhancement: A Comprehensive Survey and Beyond
This paper presents a comprehensive survey of low-light image and video
enhancement. We begin with the challenging mixed over-/under-exposed images,
which are under-performed by existing methods. To this end, we propose two
variants of the SICE dataset named SICE_Grad and SICE_Mix. Next, we introduce
Night Wenzhou, a large-scale, high-resolution video dataset, to address the
issue of the lack of a low-light video dataset that discount the use of
low-light image enhancement (LLIE) to videos. Our Night Wenzhou dataset is
challenging since it consists of fast-moving aerial scenes and streetscapes
with varying illuminations and degradation. We conduct extensive key technique
analysis and experimental comparisons for representative LLIE approaches using
these newly proposed datasets and the current benchmark datasets. Finally, we
address unresolved issues and propose future research topics for the LLIE
community. Our datasets are available at
https://github.com/ShenZheng2000/LLIE_Survey.Comment: 13 pages, 8 tables, and 13 figure
Lighting up NeRF via Unsupervised Decomposition and Enhancement
Neural Radiance Field (NeRF) is a promising approach for synthesizing novel
views, given a set of images and the corresponding camera poses of a scene.
However, images photographed from a low-light scene can hardly be used to train
a NeRF model to produce high-quality results, due to their low pixel
intensities, heavy noise, and color distortion. Combining existing low-light
image enhancement methods with NeRF methods also does not work well due to the
view inconsistency caused by the individual 2D enhancement process. In this
paper, we propose a novel approach, called Low-Light NeRF (or LLNeRF), to
enhance the scene representation and synthesize normal-light novel views
directly from sRGB low-light images in an unsupervised manner. The core of our
approach is a decomposition of radiance field learning, which allows us to
enhance the illumination, reduce noise and correct the distorted colors jointly
with the NeRF optimization process. Our method is able to produce novel view
images with proper lighting and vivid colors and details, given a collection of
camera-finished low dynamic range (8-bits/channel) images from a low-light
scene. Experiments demonstrate that our method outperforms existing low-light
enhancement methods and NeRF methods.Comment: ICCV 2023. Project website: https://whyy.site/paper/llner
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
Deep Quantigraphic Image Enhancement via Comparametric Equations
Most recent methods of deep image enhancement can be generally classified
into two types: decompose-and-enhance and illumination estimation-centric. The
former is usually less efficient, and the latter is constrained by a strong
assumption regarding image reflectance as the desired enhancement result. To
alleviate this constraint while retaining high efficiency, we propose a novel
trainable module that diversifies the conversion from the low-light image and
illumination map to the enhanced image. It formulates image enhancement as a
comparametric equation parameterized by a camera response function and an
exposure compensation ratio. By incorporating this module in an illumination
estimation-centric DNN, our method improves the flexibility of deep image
enhancement, limits the computational burden to illumination estimation, and
allows for fully unsupervised learning adaptable to the diverse demands of
different tasks.Comment: Published in ICASSP 2023. For GitHub code, see
https://github.com/nttcslab/con
A Modular Deep Learning Framework for Scene Understanding in Augmented Reality Applications
Taking as input natural images and videos augmented reality (AR) applications aim to enhance the real world with superimposed digital contents enabling interaction between the user and the environment. One important step in this process is automatic scene analysis and understanding that should be performed both in real time and with a good level of object recognition accuracy. In this work an end-to-end framework based on the combination of a Super Resolution network with a detection and recognition deep network has been proposed to increase performance and lower processing time. This novel approach has been evaluated on two different datasets: the popular COCO dataset whose real images are used for benchmarking many different computer vision tasks, and a generated dataset with synthetic images recreating a variety of environmental, lighting and acquisition conditions. The evaluation analysis is focused on small objects, which are more challenging to be correctly detected and recognised. The results show that the Average Precision is higher for smaller and low resolution objects for the proposed end-to-end approach in most of the selected conditions
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