no code implementations • 30 Nov 2023 • Hrushikesh Loya, Łukasz Dudziak, Abhinav Mehrotra, Royson Lee, Javier Fernandez-Marques, Nicholas D. Lane, Hongkai Wen
Neural architecture search has proven to be a powerful approach to designing and refining neural networks, often boosting their performance and efficiency over manually-designed variations, but comes with computational overhead.
1 code implementation • 15 Dec 2022 • Royson Lee, Rui Li, Stylianos I. Venieris, Timothy Hospedales, Ferenc Huszár, Nicholas D. Lane
Recent image degradation estimation methods have enabled single-image super-resolution (SR) approaches to better upsample real-world images.
no code implementations • 15 Dec 2022 • Stylianos I. Venieris, Mario Almeida, Royson Lee, Nicholas D. Lane
In recent years, image and video delivery systems have begun integrating deep learning super-resolution (SR) approaches, leveraging their unprecedented visual enhancement capabilities while reducing reliance on networking conditions.
no code implementations • 20 Sep 2022 • Hongxiang Fan, Thomas Chau, Stylianos I. Venieris, Royson Lee, Alexandros Kouris, Wayne Luk, Nicholas D. Lane, Mohamed S. Abdelfattah
By jointly optimizing the algorithm and hardware, our FPGA-based butterfly accelerator achieves 14. 2 to 23. 2 times speedup over state-of-the-art accelerators normalized to the same computational budget.
no code implementations • 18 Aug 2021 • Lichuan Xiang, Royson Lee, Mohamed S. Abdelfattah, Nicholas D. Lane, Hongkai Wen
Deep learning-based blind super-resolution (SR) methods have recently achieved unprecedented performance in upscaling frames with unknown degradation.
no code implementations • 7 Jun 2021 • Royson Lee, Stylianos I. Venieris, Nicholas D. Lane
In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement.
no code implementations • 12 Oct 2020 • Royson Lee, Stylianos I. Venieris, Nicholas D. Lane
In recent years, advances in the field of deep learning on tasks such as super-resolution and image enhancement have led to unprecedented performance in generating high-quality images from low-quality ones, a process we refer to as neural enhancement.
2 code implementations • NeurIPS 2020 • Łukasz Dudziak, Thomas Chau, Mohamed S. Abdelfattah, Royson Lee, Hyeji Kim, Nicholas D. Lane
What is more, we investigate prediction quality on different metrics and show that sample efficiency of the predictor-based NAS can be improved by considering binary relations of models and an iterative data selection strategy.
2 code implementations • ECCV 2020 • Royson Lee, Łukasz Dudziak, Mohamed Abdelfattah, Stylianos I. Venieris, Hyeji Kim, Hongkai Wen, Nicholas D. Lane
Recent works in single-image perceptual super-resolution (SR) have demonstrated unprecedented performance in generating realistic textures by means of deep convolutional networks.
no code implementations • 11 Feb 2020 • Mohamed S. Abdelfattah, Łukasz Dudziak, Thomas Chau, Royson Lee, Hyeji Kim, Nicholas D. Lane
We automate HW-CNN codesign using NAS by including parameters from both the CNN model and the HW accelerator, and we jointly search for the best model-accelerator pair that boosts accuracy and efficiency.
no code implementations • 21 Aug 2019 • Royson Lee, Stylianos I. Venieris, Łukasz Dudziak, Sourav Bhattacharya, Nicholas D. Lane
In recent years, convolutional networks have demonstrated unprecedented performance in the image restoration task of super-resolution (SR).
no code implementations • ICLR 2019 • Royson Lee, Nic Lane, Marko Stankovic, Sourav Bhattacharya
A successful application of convolutional architectures is to increase the resolution of single low-resolution images -- a image restoration task called super-resolution (SR).