515 research outputs found
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
Exploring Feature Representation Learning for Semi-supervised Medical Image Segmentation
This paper presents a simple yet effective two-stage framework for
semi-supervised medical image segmentation. Our key insight is to explore the
feature representation learning with labeled and unlabeled (i.e., pseudo
labeled) images to enhance the segmentation performance. In the first stage, we
present an aleatoric uncertainty-aware method, namely AUA, to improve the
segmentation performance for generating high-quality pseudo labels. Considering
the inherent ambiguity of medical images, AUA adaptively regularizes the
consistency on images with low ambiguity. To enhance the representation
learning, we propose a stage-adaptive contrastive learning method, including a
boundary-aware contrastive loss to regularize the labeled images in the first
stage and a prototype-aware contrastive loss to optimize both labeled and
pseudo labeled images in the second stage. The boundary-aware contrastive loss
only optimizes pixels around the segmentation boundaries to reduce the
computational cost. The prototype-aware contrastive loss fully leverages both
labeled images and pseudo labeled images by building a centroid for each class
to reduce computational cost for pair-wise comparison. Our method achieves the
best results on two public medical image segmentation benchmarks. Notably, our
method outperforms the prior state-of-the-art by 5.7% on Dice for colon tumor
segmentation relying on just 5% labeled images.Comment: On submission to TM
Numerical Simulation and Experimental Study of Deep Bed Corn Drying Based on Water Potential
The concept and the model of water potential, which were widely used in agricultural field, have been proved to be beneficial in the application of vacuum drying model and have provided a new way to explore the grain drying model since being introduced to grain drying and storage fields. Aiming to overcome the shortcomings of traditional deep bed drying model, for instance, the application range of this method is narrow and such method does not apply to systems of which pressure would be an influential factor such as vacuum drying system in a way combining with water potential drying model. This study established a numerical simulation system of deep bed corn drying process which has been proved to be effective according to the results of numerical simulation and corresponding experimental investigation and has revealed that desorption and adsorption coexist in deep bed drying
Angle-Aware and Tone-Aware Luminosity Analysis for Paper Model Surface
Luminosity contributes to the paper model surface perception. It has a significant impact on the perception of colour and details. The main purpose of this paper is to study the reflection luminosity of paper model surface which can be of complex or difficult shape surface. The final perception quality of a product, whether it is plain or 3D or other different shape, depends on the surface luminosity perceived by the receptor, such as eyes or measurement instruments. However, the number of parameters and limits of the paper model surface are enormous. It is a time-consuming work to select every parameter by a trial-and-error procedure. For a paper surface under the fixed lighting environment, the most important factors to decide the performance of perception are commonly viewing angles and surface tone. Therefore, the two related terms, perception angle and surface tone, were chosen to work in the analysis process. The final analysis, based on the initial conditions, enabled to predict the perception of paper model surface and to set the optimal perceived angels and tones. It still proposed the next step to model the perception of paper model surface of different shapes in a relatively short period
An Efficient Distributed Nash Equilibrium Seeking with Compressed and Event-triggered Communication
Distributed Nash equilibrium (NE) seeking problems for networked games have
been widely investigated in recent years. Despite the increasing attention,
communication expenditure is becoming a major bottleneck for scaling up
distributed approaches within limited communication bandwidth between agents.
To reduce communication cost, an efficient distributed NE seeking (ETC-DNES)
algorithm is proposed to obtain an NE for games over directed graphs, where the
communication efficiency is improved by event-triggered exchanges of compressed
information among neighbors. ETC-DNES saves communication costs in both
transmitted bits and rounds of communication. Furthermore, our method only
requires the row-stochastic property of the adjacency matrix, unlike previous
approaches that hinged on doubly-stochastic communication matrices. We provide
convergence guarantees for ETC-DNES on games with restricted strongly monotone
mappings and testify its efficiency with no sacrifice on the accuracy. The
algorithm and analysis are extended to a compressed algorithm with stochastic
event-triggered mechanism (SETC-DNES). In SETC-DNES, we introduce a random
variable in the triggering condition to further enhance algorithm efficiency.
We demonstrate that SETC-DNES guarantees linear convergence to the NE while
achieving even greater reductions in communication costs compared to ETC-DNES.
Finally, numerical simulations illustrate the effectiveness of the proposed
algorithms
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