Jun 15, 2022 · Abstract:The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, (e.g., ...
We propose an integral VSR pruning scheme called. Structured Sparsity Learning (SSL) for various com- ponents of VSR models, such as residual blocks, re-.
In SSL, we design pruning schemes for several key components in VSR models, including residual blocks, re- current networks, and upsampling networks.
Super-Resolving (SR) video is more challenging compared with image super-resolution because of the demanding computation time. To enlarge a low-resolution video ...
This work reveals the relative sparsity effect in emerging stimulative training and proposes a structured pruning framework, named STP, based on an enhanced ...
The high computational costs of video super-resolution (VSR) models hinder their deployment on resource-limited devices, e.g., smartphones and drones.
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To pursue more efficient image SR networks, we then propose aligned structured sparsity learning (ASSL) to train lightweight model, resulting in ASSLN. 3.1 Deep ...
Jun 10, 2024 · We apply aligned structured sparsity learning strategy to train efficient image SR network, named as ASSLN, with smaller model size and lower ...
Abstract. Lightweight image super-resolution (SR) networks have obtained promising results with moderate model size. Many SR methods have focused on designing ...
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In this survey, we comprehensively investigate 33 state-of-the-art video super-resolution (VSR) methods based on deep learning. It is well known that the ...