127 research outputs found
A Large-scale Film Style Dataset for Learning Multi-frequency Driven Film Enhancement
Film, a classic image style, is culturally significant to the whole
photographic industry since it marks the birth of photography. However, film
photography is time-consuming and expensive, necessitating a more efficient
method for collecting film-style photographs. Numerous datasets that have
emerged in the field of image enhancement so far are not film-specific. In
order to facilitate film-based image stylization research, we construct
FilmSet, a large-scale and high-quality film style dataset. Our dataset
includes three different film types and more than 5000 in-the-wild high
resolution images. Inspired by the features of FilmSet images, we propose a
novel framework called FilmNet based on Laplacian Pyramid for stylizing images
across frequency bands and achieving film style outcomes. Experiments reveal
that the performance of our model is superior than state-of-the-art techniques.
Our dataset and code will be made publicly available
-factorized differential equations for two-loop non-planar triangle Feynman integrals with elliptic curves
In this paper, we investigate two-loop non-planar triangle Feynman integrals
involving elliptic curves. In contrast to the Sunrise and Banana integral
families, the triangle families involve non-trivial sub-sectors. We show that
the methodology developed in the context of Banana integrals can also be
extended to these cases and obtain -factorized differential
equations for all sectors. The letters are combinations of modular forms on the
corresponding elliptic curves and algebraic functions arising from the
sub-sectors. With uniform transcendental boundary conditions, we express our
results in terms of iterated integrals order-by-order in the dimensional
regulator, which can be evaluated efficiently. Our method can be
straightforwardly generalized to other elliptic integral families and have
important applications to precision physics at current and future high-energy
colliders.Comment: Journal version. Add the Lambert series for the Y-invarian
High-Resolution Document Shadow Removal via A Large-Scale Real-World Dataset and A Frequency-Aware Shadow Erasing Net
Shadows often occur when we capture the documents with casual equipment,
which influences the visual quality and readability of the digital copies.
Different from the algorithms for natural shadow removal, the algorithms in
document shadow removal need to preserve the details of fonts and figures in
high-resolution input. Previous works ignore this problem and remove the
shadows via approximate attention and small datasets, which might not work in
real-world situations. We handle high-resolution document shadow removal
directly via a larger-scale real-world dataset and a carefully designed
frequency-aware network. As for the dataset, we acquire over 7k couples of
high-resolution (2462 x 3699) images of real-world document pairs with various
samples under different lighting circumstances, which is 10 times larger than
existing datasets. As for the design of the network, we decouple the
high-resolution images in the frequency domain, where the low-frequency details
and high-frequency boundaries can be effectively learned via the carefully
designed network structure. Powered by our network and dataset, the proposed
method clearly shows a better performance than previous methods in terms of
visual quality and numerical results. The code, models, and dataset are
available at: https://github.com/CXH-Research/DocShadow-SD7KComment: Accepted by International Conference on Computer Vision 2023 (ICCV
2023
-forms for non-planar triangles with elliptic curves at two loops
In this talk, we discuss how to generalize ideas developed for Banana
integrals to two two-loop non-planar triangle Feynman integrals involving
elliptic curves, which have non-trivial sub-sectors and whose Picard-Fuchs
operators share less symmetry than Banana integrals, to obtain the canonical
differential equations and to solve them with suitable boundary conditions.Comment: RADCOR202
ShaDocFormer: A Shadow-attentive Threshold Detector with Cascaded Fusion Refiner for document shadow removal
Document shadow is a common issue that arise when capturing documents using
mobile devices, which significantly impacts the readability. Current methods
encounter various challenges including inaccurate detection of shadow masks and
estimation of illumination. In this paper, we propose ShaDocFormer, a
Transformer-based architecture that integrates traditional methodologies and
deep learning techniques to tackle the problem of document shadow removal. The
ShaDocFormer architecture comprises two components: the Shadow-attentive
Threshold Detector (STD) and the Cascaded Fusion Refiner (CFR). The STD module
employs a traditional thresholding technique and leverages the attention
mechanism of the Transformer to gather global information, thereby enabling
precise detection of shadow masks. The cascaded and aggregative structure of
the CFR module facilitates a coarse-to-fine restoration process for the entire
image. As a result, ShaDocFormer excels in accurately detecting and capturing
variations in both shadow and illumination, thereby enabling effective removal
of shadows. Extensive experiments demonstrate that ShaDocFormer outperforms
current state-of-the-art methods in both qualitative and quantitative
measurements
DocDeshadower: Frequency-aware Transformer for Document Shadow Removal
The presence of shadows significantly impacts the visual quality of scanned
documents. However, the existing traditional techniques and deep learning
methods used for shadow removal have several limitations. These methods either
rely heavily on heuristics, resulting in suboptimal performance, or require
large datasets to learn shadow-related features. In this study, we propose the
DocDeshadower, a multi-frequency Transformer-based model built on Laplacian
Pyramid. DocDeshadower is designed to remove shadows at different frequencies
in a coarse-to-fine manner. To achieve this, we decompose the shadow image into
different frequency bands using Laplacian Pyramid. In addition, we introduce
two novel components to this model: the Attention-Aggregation Network and the
Gated Multi-scale Fusion Transformer. The Attention-Aggregation Network is
designed to remove shadows in the low-frequency part of the image, whereas the
Gated Multi-scale Fusion Transformer refines the entire image at a global scale
with its large perceptive field. Our extensive experiments demonstrate that
DocDeshadower outperforms the current state-of-the-art methods in both
qualitative and quantitative terms
UWFormer: Underwater Image Enhancement via a Semi-Supervised Multi-Scale Transformer
Underwater images often exhibit poor quality, imbalanced coloration, and low
contrast due to the complex and intricate interaction of light, water, and
objects. Despite the significant contributions of previous underwater
enhancement techniques, there exist several problems that demand further
improvement: (i) Current deep learning methodologies depend on Convolutional
Neural Networks (CNNs) that lack multi-scale enhancement and also have limited
global perception fields. (ii) The scarcity of paired real-world underwater
datasets poses a considerable challenge, and the utilization of synthetic image
pairs risks overfitting. To address the aforementioned issues, this paper
presents a Multi-scale Transformer-based Network called UWFormer for enhancing
images at multiple frequencies via semi-supervised learning, in which we
propose a Nonlinear Frequency-aware Attention mechanism and a Multi-Scale
Fusion Feed-forward Network for low-frequency enhancement. Additionally, we
introduce a specialized underwater semi-supervised training strategy, proposing
a Subaqueous Perceptual Loss function to generate reliable pseudo labels.
Experiments using full-reference and non-reference underwater benchmarks
demonstrate that our method outperforms state-of-the-art methods in terms of
both quantity and visual quality
DiffGAN-F2S: Symmetric and Efficient Denoising Diffusion GANs for Structural Connectivity Prediction from Brain fMRI
Mapping from functional connectivity (FC) to structural connectivity (SC) can
facilitate multimodal brain network fusion and discover potential biomarkers
for clinical implications. However, it is challenging to directly bridge the
reliable non-linear mapping relations between SC and functional magnetic
resonance imaging (fMRI). In this paper, a novel diffusision generative
adversarial network-based fMRI-to-SC (DiffGAN-F2S) model is proposed to predict
SC from brain fMRI in an end-to-end manner. To be specific, the proposed
DiffGAN-F2S leverages denoising diffusion probabilistic models (DDPMs) and
adversarial learning to efficiently generate high-fidelity SC through a few
steps from fMRI. By designing the dual-channel multi-head spatial attention
(DMSA) and graph convolutional modules, the symmetric graph generator first
captures global relations among direct and indirect connected brain regions,
then models the local brain region interactions. It can uncover the complex
mapping relations between fMRI and structural connectivity. Furthermore, the
spatially connected consistency loss is devised to constrain the generator to
preserve global-local topological information for accurate intrinsic SC
prediction. Testing on the public Alzheimer's Disease Neuroimaging Initiative
(ADNI) dataset, the proposed model can effectively generate empirical
SC-preserved connectivity from four-dimensional imaging data and shows superior
performance in SC prediction compared with other related models. Furthermore,
the proposed model can identify the vast majority of important brain regions
and connections derived from the empirical method, providing an alternative way
to fuse multimodal brain networks and analyze clinical disease.Comment: 12 page
Caenorhabditis elegans methionine/S-adenosylmethionine cycle activity is sensed and adjusted by a nuclear hormone receptor
Vitamin B12 is an essential micronutrient that functions in two metabolic pathways: the canonical propionate breakdown pathway and the methionine/S-adenosylmethionine (Met/SAM) cycle. In Caenorhabditis elegans, low vitamin B12, or genetic perturbation of the canonical propionate breakdown pathway results in propionate accumulation and the transcriptional activation of a propionate shunt pathway. This propionate-dependent mechanism requires nhr-10 and is referred to as \u27B12-mechanism-I\u27. Here, we report that vitamin B12 represses the expression of Met/SAM cycle genes by a propionate-independent mechanism we refer to as \u27B12-mechanism-II\u27. This mechanism is activated by perturbations in the Met/SAM cycle, genetically or due to low dietary vitamin B12. B12-mechanism-II requires nhr-114 to activate Met/SAM cycle gene expression, the vitamin B12 transporter, pmp-5, and adjust influx and efflux of the cycle by activating msra-1 and repressing cbs-1, respectively. Taken together, Met/SAM cycle activity is sensed and transcriptionally adjusted to be in a tight metabolic regime
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