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Parichehr Behjati Ardakani; Pau Rodriguez; Carles Fernandez; Armin Mehri; Xavier Roca; Seiichi Ozawa; Jordi Gonzalez |
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Title |
Frequency-based Enhancement Network for Efficient Super-Resolution |
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Journal Article |
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Year |
2022 |
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IEEE Access |
Abbreviated Journal |
ACCESS |
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10 |
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57383-57397 |
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Keywords |
Deep learning; Frequency-based methods; Lightweight architectures; Single image super-resolution |
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Abstract |
Recently, deep convolutional neural networks (CNNs) have provided outstanding performance in single image super-resolution (SISR). Despite their remarkable performance, the lack of high-frequency information in the recovered images remains a core problem. Moreover, as the networks increase in depth and width, deep CNN-based SR methods are faced with the challenge of computational complexity in practice. A promising and under-explored solution is to adapt the amount of compute based on the different frequency bands of the input. To this end, we present a novel Frequency-based Enhancement Block (FEB) which explicitly enhances the information of high frequencies while forwarding low-frequencies to the output. In particular, this block efficiently decomposes features into low- and high-frequency and assigns more computation to high-frequency ones. Thus, it can help the network generate more discriminative representations by explicitly recovering finer details. Our FEB design is simple and generic and can be used as a direct replacement of commonly used SR blocks with no need to change network architectures. We experimentally show that when replacing SR blocks with FEB we consistently improve the reconstruction error, while reducing the number of parameters in the model. Moreover, we propose a lightweight SR model — Frequency-based Enhancement Network (FENet) — based on FEB that matches the performance of larger models. Extensive experiments demonstrate that our proposal performs favorably against the state-of-the-art SR algorithms in terms of visual quality, memory footprint, and inference time. The code is available at https://github.com/pbehjatii/FENet |
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18 May 2022 |
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IEEE |
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no |
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Admin @ si @ BRF2022a |
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3747 |
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Author |
Xavier Soria; Gonzalo Pomboza-Junez; Angel Sappa |
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Title |
LDC: Lightweight Dense CNN for Edge Detection |
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Journal Article |
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Year |
2022 |
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IEEE Access |
Abbreviated Journal |
ACCESS |
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10 |
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68281-68290 |
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This paper presents a Lightweight Dense Convolutional (LDC) neural network for edge detection. The proposed model is an adaptation of two state-of-the-art approaches, but it requires less than 4% of parameters in comparison with these approaches. The proposed architecture generates thin edge maps and reaches the highest score (i.e., ODS) when compared with lightweight models (models with less than 1 million parameters), and reaches a similar performance when compare with heavy architectures (models with about 35 million parameters). Both quantitative and qualitative results and comparisons with state-of-the-art models, using different edge detection datasets, are provided. The proposed LDC does not use pre-trained weights and requires straightforward hyper-parameter settings. The source code is released at https://github.com/xavysp/LDC |
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27 June 2022 |
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IEEE |
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MSIAU; MACO; 600.160; 600.167 |
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Admin @ si @ SPS2022 |
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3751 |
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Author |
David Castells; Vinh Ngo; Juan Borrego-Carazo; Marc Codina; Carles Sanchez; Debora Gil; Jordi Carrabina |
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Title |
A Survey of FPGA-Based Vision Systems for Autonomous Cars |
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Journal Article |
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2022 |
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IEEE Access |
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ACESS |
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10 |
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132525-132563 |
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Autonomous automobile; Computer vision; field programmable gate arrays; reconfigurable architectures |
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On the road to making self-driving cars a reality, academic and industrial researchers are working hard to continue to increase safety while meeting technical and regulatory constraints Understanding the surrounding environment is a fundamental task in self-driving cars. It requires combining complex computer vision algorithms. Although state-of-the-art algorithms achieve good accuracy, their implementations often require powerful computing platforms with high power consumption. In some cases, the processing speed does not meet real-time constraints. FPGA platforms are often used to implement a category of latency-critical algorithms that demand maximum performance and energy efficiency. Since self-driving car computer vision functions fall into this category, one could expect to see a wide adoption of FPGAs in autonomous cars. In this paper, we survey the computer vision FPGA-based works from the literature targeting automotive applications over the last decade. Based on the survey, we identify the strengths and weaknesses of FPGAs in this domain and future research opportunities and challenges. |
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16 December 2022 |
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IEEE |
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IAM; 600.166 |
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Admin @ si @ CNB2022 |
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3760 |
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