17,679 research outputs found
DeepFuse: A Deep Unsupervised Approach for Exposure Fusion with Extreme Exposure Image Pairs
We present a novel deep learning architecture for fusing static
multi-exposure images. Current multi-exposure fusion (MEF) approaches use
hand-crafted features to fuse input sequence. However, the weak hand-crafted
representations are not robust to varying input conditions. Moreover, they
perform poorly for extreme exposure image pairs. Thus, it is highly desirable
to have a method that is robust to varying input conditions and capable of
handling extreme exposure without artifacts. Deep representations have known to
be robust to input conditions and have shown phenomenal performance in a
supervised setting. However, the stumbling block in using deep learning for MEF
was the lack of sufficient training data and an oracle to provide the
ground-truth for supervision. To address the above issues, we have gathered a
large dataset of multi-exposure image stacks for training and to circumvent the
need for ground truth images, we propose an unsupervised deep learning
framework for MEF utilizing a no-reference quality metric as loss function. The
proposed approach uses a novel CNN architecture trained to learn the fusion
operation without reference ground truth image. The model fuses a set of common
low level features extracted from each image to generate artifact-free
perceptually pleasing results. We perform extensive quantitative and
qualitative evaluation and show that the proposed technique outperforms
existing state-of-the-art approaches for a variety of natural images.Comment: ICCV 201
デバイスの限界を超えた正確な撮像を可能にする深層学習
Tohoku University博士(情報科学)thesi
A Perceptually Optimized and Self-Calibrated Tone Mapping Operator
With the increasing popularity and accessibility of high dynamic range (HDR)
photography, tone mapping operators (TMOs) for dynamic range compression are
practically demanding. In this paper, we develop a two-stage neural
network-based TMO that is self-calibrated and perceptually optimized. In Stage
one, motivated by the physiology of the early stages of the human visual
system, we first decompose an HDR image into a normalized Laplacian pyramid. We
then use two lightweight deep neural networks (DNNs), taking the normalized
representation as input and estimating the Laplacian pyramid of the
corresponding LDR image. We optimize the tone mapping network by minimizing the
normalized Laplacian pyramid distance (NLPD), a perceptual metric aligning with
human judgments of tone-mapped image quality. In Stage two, the input HDR image
is self-calibrated to compute the final LDR image. We feed the same HDR image
but rescaled with different maximum luminances to the learned tone mapping
network, and generate a pseudo-multi-exposure image stack with different detail
visibility and color saturation. We then train another lightweight DNN to fuse
the LDR image stack into a desired LDR image by maximizing a variant of the
structural similarity index for multi-exposure image fusion (MEF-SSIM), which
has been proven perceptually relevant to fused image quality. The proposed
self-calibration mechanism through MEF enables our TMO to accept uncalibrated
HDR images, while being physiology-driven. Extensive experiments show that our
method produces images with consistently better visual quality. Additionally,
since our method builds upon three lightweight DNNs, it is among the fastest
local TMOs.Comment: 20 pages,18 figure
Quantification of DNA-associated proteins inside eukaryotic cells using single-molecule localization microscopy
Development of single-molecule localization microscopy techniques has allowed nanometre scale localization accuracy inside cells, permitting the resolution of ultra-fine cell structure and the elucidation of crucial molecular mechanisms. Application of these methodologies to understanding processes underlying DNA replication and repair has been limited to defined in vitro biochemical analysis and prokaryotic cells. In order to expand these techniques to eukaryotic systems, we have further developed a photo-activated localization microscopy-based method to directly visualize DNA-associated proteins in unfixed eukaryotic cells. We demonstrate that motion blurring of fluorescence due to protein diffusivity can be used to selectively image the DNA-bound population of proteins. We designed and tested a simple methodology and show that it can be used to detect changes in DNA binding of a replicative helicase subunit, Mcm4, and the replication sliding clamp, PCNA, between different stages of the cell cycle and between distinct genetic backgrounds
Exposure Fusion for Hand-held Camera Inputs with Optical Flow and PatchMatch
This paper proposes a hybrid synthesis method for multi-exposure image fusion
taken by hand-held cameras. Motions either due to the shaky camera or caused by
dynamic scenes should be compensated before any content fusion. Any
misalignment can easily cause blurring/ghosting artifacts in the fused result.
Our hybrid method can deal with such motions and maintain the exposure
information of each input effectively. In particular, the proposed method first
applies optical flow for a coarse registration, which performs well with
complex non-rigid motion but produces deformations at regions with missing
correspondences. The absence of correspondences is due to the occlusions of
scene parallax or the moving contents. To correct such error registration, we
segment images into superpixels and identify problematic alignments based on
each superpixel, which is further aligned by PatchMatch. The method combines
the efficiency of optical flow and the accuracy of PatchMatch. After PatchMatch
correction, we obtain a fully aligned image stack that facilitates a
high-quality fusion that is free from blurring/ghosting artifacts. We compare
our method with existing fusion algorithms on various challenging examples,
including the static/dynamic, the indoor/outdoor and the daytime/nighttime
scenes. Experiment results demonstrate the effectiveness and robustness of our
method
A new multicompartmental reaction-diffusion modeling method links transient membrane attachment of E. coli MinE to E-ring formation
Many important cellular processes are regulated by reaction-diffusion (RD) of molecules that takes place both in the cytoplasm and on the membrane. To model and analyze such multicompartmental processes, we developed a lattice-based Monte Carlo method, Spatiocyte that supports RD in volume and surface compartments at single molecule resolution. Stochasticity in RD and the excluded volume effect brought by intracellular molecular crowding, both of which can significantly affect RD and thus, cellular processes, are also supported. We verified the method by comparing simulation results of diffusion, irreversible and reversible reactions with the predicted analytical and best available numerical solutions. Moreover, to directly compare the localization patterns of molecules in fluorescence microscopy images with simulation, we devised a visualization method that mimics the microphotography process by showing the trajectory of simulated molecules averaged according to the camera exposure time. In the rod-shaped bacterium _Escherichia coli_, the division site is suppressed at the cell poles by periodic pole-to-pole oscillations of the Min proteins (MinC, MinD and MinE) arising from carefully orchestrated RD in both cytoplasm and membrane compartments. Using Spatiocyte we could model and reproduce the _in vivo_ MinDE localization dynamics by accounting for the established properties of MinE. Our results suggest that the MinE ring, which is essential in preventing polar septation, is largely composed of MinE that is transiently attached to the membrane independently after recruited by MinD. Overall, Spatiocyte allows simulation and visualization of complex spatial and reaction-diffusion mediated cellular processes in volumes and surfaces. As we showed, it can potentially provide mechanistic insights otherwise difficult to obtain experimentally
Hybrid-Supervised Dual-Search: Leveraging Automatic Learning for Loss-free Multi-Exposure Image Fusion
Multi-exposure image fusion (MEF) has emerged as a prominent solution to
address the limitations of digital imaging in representing varied exposure
levels. Despite its advancements, the field grapples with challenges, notably
the reliance on manual designs for network structures and loss functions, and
the constraints of utilizing simulated reference images as ground truths.
Consequently, current methodologies often suffer from color distortions and
exposure artifacts, further complicating the quest for authentic image
representation. In addressing these challenges, this paper presents a
Hybrid-Supervised Dual-Search approach for MEF, dubbed HSDS-MEF, which
introduces a bi-level optimization search scheme for automatic design of both
network structures and loss functions. More specifically, we harnesses a unique
dual research mechanism rooted in a novel weighted structure refinement
architecture search. Besides, a hybrid supervised contrast constraint
seamlessly guides and integrates with searching process, facilitating a more
adaptive and comprehensive search for optimal loss functions. We realize the
state-of-the-art performance in comparison to various competitive schemes,
yielding a 10.61% and 4.38% improvement in Visual Information Fidelity (VIF)
for general and no-reference scenarios, respectively, while providing results
with high contrast, rich details and colors
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