429 research outputs found
Implementing a structural continuity constraint and a halting method for the topology optimization of energy absorbers
FastLLVE: Real-Time Low-Light Video Enhancement with Intensity-Aware Lookup Table
Low-Light Video Enhancement (LLVE) has received considerable attention in
recent years. One of the critical requirements of LLVE is inter-frame
brightness consistency, which is essential for maintaining the temporal
coherence of the enhanced video. However, most existing single-image-based
methods fail to address this issue, resulting in flickering effect that
degrades the overall quality after enhancement. Moreover, 3D Convolution Neural
Network (CNN)-based methods, which are designed for video to maintain
inter-frame consistency, are computationally expensive, making them impractical
for real-time applications. To address these issues, we propose an efficient
pipeline named FastLLVE that leverages the Look-Up-Table (LUT) technique to
maintain inter-frame brightness consistency effectively. Specifically, we
design a learnable Intensity-Aware LUT (IA-LUT) module for adaptive
enhancement, which addresses the low-dynamic problem in low-light scenarios.
This enables FastLLVE to perform low-latency and low-complexity enhancement
operations while maintaining high-quality results. Experimental results on
benchmark datasets demonstrate that our method achieves the State-Of-The-Art
(SOTA) performance in terms of both image quality and inter-frame brightness
consistency. More importantly, our FastLLVE can process 1,080p videos at
Frames Per Second (FPS), which is faster
than SOTA CNN-based methods in inference time, making it a promising solution
for real-time applications. The code is available at
https://github.com/Wenhao-Li-777/FastLLVE.Comment: 11pages, 9 Figures, and 6 Tables. Accepted by ACMMM 202
Information Bottleneck Revisited: Posterior Probability Perspective with Optimal Transport
Information bottleneck (IB) is a paradigm to extract information in one
target random variable from another relevant random variable, which has aroused
great interest due to its potential to explain deep neural networks in terms of
information compression and prediction. Despite its great importance, finding
the optimal bottleneck variable involves a difficult nonconvex optimization
problem due to the nonconvexity of mutual information constraint. The
Blahut-Arimoto algorithm and its variants provide an approach by considering
its Lagrangian with fixed Lagrange multiplier. However, only the strictly
concave IB curve can be fully obtained by the BA algorithm, which strongly
limits its application in machine learning and related fields, as strict
concavity cannot be guaranteed in those problems. To overcome the above
difficulty, we derive an entropy regularized optimal transport (OT) model for
IB problem from a posterior probability perspective. Correspondingly, we use
the alternating optimization procedure and generalize the Sinkhorn algorithm to
solve the above OT model. The effectiveness and efficiency of our approach are
demonstrated via numerical experiments.Comment: ISIT 202
Robust prior-based single image super resolution under multiple Gaussian degradations
Although SISR (Single Image Super Resolution) problem can be effectively solved by deep learning based methods, the training phase often considers single degradation type such as bicubic interpolation or Gaussian blur with fixed variance. These priori hypotheses often fail and lead to reconstruction error in real scenario. In this paper, we propose an end-to-end CNN model RPSRMD to handle SR problem in multiple Gaussian degradations by extracting and using as side information a shared image prior that is consistent in different Gaussian degradations. The shared image prior is generated by an AED network RPGen with a rationally designed loss function that contains two parts: consistency loss and validity loss. These losses supervise the training of AED to guarantee that the image priors of one image with different Gaussian blurs to be very similar. Afterwards we carefully designed a SR network, which is termed as PResNet (Prior based Residual Network) in this paper, to efficiently use the image priors and generate high quality and robust SR images when unknown Gaussian blur is presented. When we applied variant Gaussian blurs to the low resolution images, the experiments prove that our proposed RPSRMD, which includes RPGen and PResNet as two core components, is superior to many state-of-the-art SR methods that were designed and trained to handle multi-degradation
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