2,354 research outputs found
Improving Extreme Low-light Image Denoising via Residual Learning
Title from PDF of title page viewed January 7, 2020Thesis advisor: Zhu LiVitaIncludes bibliographical references (page 21-23)Thesis (M.S.)--School of Computing and Engineering. University of Missouri--Kansas City, 2019Taking a satisfactory picture in a low-light environment remains a challenging problem. Low-light imaging mainly suffers from noise due to the low signal-to-noise ratio. Many methods have been proposed for the task of image denoising, but they fail to work with the noise under extremely low light conditions. Recently, deep learning based approaches have been presented that have higher objective quality than traditional methods, but they usually have high computation cost which makes them impractical to use in real-time applications or where the computational resource is limited. In this paper, we propose a new residual learning based deep neural network for end-to-end extreme low-light image denoising that can not only significantly reduce the computational cost but also improve the performance over existing methods in both objective and subjective metrics. Specifically, in one setting we achieved 29x speedup with higher PSNR. Subjectively, our method provides better color reproduction and preserves more detailed texture information compared to state of the art methods.Introduction -- Related work -- Method -- Experiments -- Conclusio
xUnit: Learning a Spatial Activation Function for Efficient Image Restoration
In recent years, deep neural networks (DNNs) achieved unprecedented
performance in many low-level vision tasks. However, state-of-the-art results
are typically achieved by very deep networks, which can reach tens of layers
with tens of millions of parameters. To make DNNs implementable on platforms
with limited resources, it is necessary to weaken the tradeoff between
performance and efficiency. In this paper, we propose a new activation unit,
which is particularly suitable for image restoration problems. In contrast to
the widespread per-pixel activation units, like ReLUs and sigmoids, our unit
implements a learnable nonlinear function with spatial connections. This
enables the net to capture much more complex features, thus requiring a
significantly smaller number of layers in order to reach the same performance.
We illustrate the effectiveness of our units through experiments with
state-of-the-art nets for denoising, de-raining, and super resolution, which
are already considered to be very small. With our approach, we are able to
further reduce these models by nearly 50% without incurring any degradation in
performance.Comment: Conference on Computer Vision and Pattern Recognition (CVPR), 201
Burstormer: Burst Image Restoration and Enhancement Transformer
On a shutter press, modern handheld cameras capture multiple images in rapid
succession and merge them to generate a single image. However, individual
frames in a burst are misaligned due to inevitable motions and contain multiple
degradations. The challenge is to properly align the successive image shots and
merge their complimentary information to achieve high-quality outputs. Towards
this direction, we propose Burstormer: a novel transformer-based architecture
for burst image restoration and enhancement. In comparison to existing works,
our approach exploits multi-scale local and non-local features to achieve
improved alignment and feature fusion. Our key idea is to enable inter-frame
communication in the burst neighborhoods for information aggregation and
progressive fusion while modeling the burst-wide context. However, the input
burst frames need to be properly aligned before fusing their information.
Therefore, we propose an enhanced deformable alignment module for aligning
burst features with regards to the reference frame. Unlike existing methods,
the proposed alignment module not only aligns burst features but also exchanges
feature information and maintains focused communication with the reference
frame through the proposed reference-based feature enrichment mechanism, which
facilitates handling complex motions. After multi-level alignment and
enrichment, we re-emphasize on inter-frame communication within burst using a
cyclic burst sampling module. Finally, the inter-frame information is
aggregated using the proposed burst feature fusion module followed by
progressive upsampling. Our Burstormer outperforms state-of-the-art methods on
burst super-resolution, burst denoising and burst low-light enhancement. Our
codes and pretrained models are available at https://
github.com/akshaydudhane16/BurstormerComment: Accepted at CVPR 202
A Comprehensive Survey of Deep Learning in Remote Sensing: Theories, Tools and Challenges for the Community
In recent years, deep learning (DL), a re-branding of neural networks (NNs),
has risen to the top in numerous areas, namely computer vision (CV), speech
recognition, natural language processing, etc. Whereas remote sensing (RS)
possesses a number of unique challenges, primarily related to sensors and
applications, inevitably RS draws from many of the same theories as CV; e.g.,
statistics, fusion, and machine learning, to name a few. This means that the RS
community should be aware of, if not at the leading edge of, of advancements
like DL. Herein, we provide the most comprehensive survey of state-of-the-art
RS DL research. We also review recent new developments in the DL field that can
be used in DL for RS. Namely, we focus on theories, tools and challenges for
the RS community. Specifically, we focus on unsolved challenges and
opportunities as it relates to (i) inadequate data sets, (ii)
human-understandable solutions for modelling physical phenomena, (iii) Big
Data, (iv) non-traditional heterogeneous data sources, (v) DL architectures and
learning algorithms for spectral, spatial and temporal data, (vi) transfer
learning, (vii) an improved theoretical understanding of DL systems, (viii)
high barriers to entry, and (ix) training and optimizing the DL.Comment: 64 pages, 411 references. To appear in Journal of Applied Remote
Sensin
Physics-guided Noise Neural Proxy for Low-light Raw Image Denoising
Low-light raw image denoising plays a crucial role in mobile photography, and
learning-based methods have become the mainstream approach. Training the
learning-based methods with synthetic data emerges as an efficient and
practical alternative to paired real data. However, the quality of synthetic
data is inherently limited by the low accuracy of the noise model, which
decreases the performance of low-light raw image denoising. In this paper, we
develop a novel framework for accurate noise modeling that learns a
physics-guided noise neural proxy (PNNP) from dark frames. PNNP integrates
three efficient techniques: physics-guided noise decoupling (PND),
physics-guided proxy model (PPM), and differentiable distribution-oriented loss
(DDL). The PND decouples the dark frame into different components and handles
different levels of noise in a flexible manner, which reduces the complexity of
the noise neural proxy. The PPM incorporates physical priors to effectively
constrain the generated noise, which promotes the accuracy of the noise neural
proxy. The DDL provides explicit and reliable supervision for noise modeling,
which promotes the precision of the noise neural proxy. Extensive experiments
on public low-light raw image denoising datasets and real low-light imaging
scenarios demonstrate the superior performance of our PNNP framework
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Efficient Burst Raw Denoising with Variance Stabilization and Multi-frequency Denoising Network
With the growing popularity of smartphones, capturing high-quality images is
of vital importance to smartphones. The cameras of smartphones have small
apertures and small sensor cells, which lead to the noisy images in low light
environment. Denoising based on a burst of multiple frames generally
outperforms single frame denoising but with the larger compututional cost. In
this paper, we propose an efficient yet effective burst denoising system. We
adopt a three-stage design: noise prior integration, multi-frame alignment and
multi-frame denoising. First, we integrate noise prior by pre-processing raw
signals into a variance-stabilization space, which allows using a small-scale
network to achieve competitive performance. Second, we observe that it is
essential to adopt an explicit alignment for burst denoising, but it is not
necessary to integrate a learning-based method to perform multi-frame
alignment. Instead, we resort to a conventional and efficient alignment method
and combine it with our multi-frame denoising network. At last, we propose a
denoising strategy that processes multiple frames sequentially. Sequential
denoising avoids filtering a large number of frames by decomposing multiple
frames denoising into several efficient sub-network denoising. As for each
sub-network, we propose an efficient multi-frequency denoising network to
remove noise of different frequencies. Our three-stage design is efficient and
shows strong performance on burst denoising. Experiments on synthetic and real
raw datasets demonstrate that our method outperforms state-of-the-art methods,
with less computational cost. Furthermore, the low complexity and high-quality
performance make deployment on smartphones possible.Comment: Accepted for publication in International Journal of Computer Visio
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