140 research outputs found
Planar graphs without cycles of length 4, 7, 8, or 9 are 3-choosable
AbstractIt is known that planar graphs without cycles of length 4, i, j, or 9 with 4<i<j<9, except that i=7 and j=8, are 3-choosable. This paper proves that planar graphs without cycles of length 4, 7, 8, or 9 are also 3-choosable
Study on application of high-K dielectric materials for discrete charge storage memory
Ph.DDOCTOR OF PHILOSOPH
OccCasNet: Occlusion-aware Cascade Cost Volume for Light Field Depth Estimation
Light field (LF) depth estimation is a crucial task with numerous practical
applications. However, mainstream methods based on the multi-view stereo (MVS)
are resource-intensive and time-consuming as they need to construct a finer
cost volume. To address this issue and achieve a better trade-off between
accuracy and efficiency, we propose an occlusion-aware cascade cost volume for
LF depth (disparity) estimation. Our cascaded strategy reduces the sampling
number while keeping the sampling interval constant during the construction of
a finer cost volume. We also introduce occlusion maps to enhance accuracy in
constructing the occlusion-aware cost volume. Specifically, we first obtain the
coarse disparity map through the coarse disparity estimation network. Then, the
sub-aperture images (SAIs) of side views are warped to the center view based on
the initial disparity map. Next, we propose photo-consistency constraints
between the warped SAIs and the center SAI to generate occlusion maps for each
SAI. Finally, we introduce the coarse disparity map and occlusion maps to
construct an occlusion-aware refined cost volume, enabling the refined
disparity estimation network to yield a more precise disparity map. Extensive
experiments demonstrate the effectiveness of our method. Compared with
state-of-the-art methods, our method achieves a superior balance between
accuracy and efficiency and ranks first in terms of MSE and Q25 metrics among
published methods on the HCI 4D benchmark. The code and model of the proposed
method are available at https://github.com/chaowentao/OccCasNet
LFSRDiff: Light Field Image Super-Resolution via Diffusion Models
Light field (LF) image super-resolution (SR) is a challenging problem due to
its inherent ill-posed nature, where a single low-resolution (LR) input LF
image can correspond to multiple potential super-resolved outcomes. Despite
this complexity, mainstream LF image SR methods typically adopt a deterministic
approach, generating only a single output supervised by pixel-wise loss
functions. This tendency often results in blurry and unrealistic results.
Although diffusion models can capture the distribution of potential SR results
by iteratively predicting Gaussian noise during the denoising process, they are
primarily designed for general images and struggle to effectively handle the
unique characteristics and information present in LF images. To address these
limitations, we introduce LFSRDiff, the first diffusion-based LF image SR
model, by incorporating the LF disentanglement mechanism. Our novel
contribution includes the introduction of a disentangled U-Net for diffusion
models, enabling more effective extraction and fusion of both spatial and
angular information within LF images. Through comprehensive experimental
evaluations and comparisons with the state-of-the-art LF image SR methods, the
proposed approach consistently produces diverse and realistic SR results. It
achieves the highest perceptual metric in terms of LPIPS. It also demonstrates
the ability to effectively control the trade-off between perception and
distortion. The code is available at
\url{https://github.com/chaowentao/LFSRDiff}
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