To solve these issues, in this paper, we design channel hourglass residual structure (CHRS) consisted of several nested residual modules for reducing parameters ...
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What is single image super-resolution?
What is single image super-resolution vs multiple image super-resolution?
A channel hourglass residual structure (CHRS), consisting of several nested residual modules, is developed to learn more discriminative representations with ...
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The use of deep convolutional neural networks (CNNs) for image super-resolution (SR) from low-resolution (LR) input has achieved remarkable reconstruction ...
Jan 1, 2022 · To solve these issues, in this paper, we design a channel hourglass residual structure (CHRS) and explore an efficient channel attention (ECA) ...
We propose a powerful but lightweight blueprint residual network (LBRN). •. We propose a novel module named LBRB to learn high-frequency information. •.
This model, named Image Super-Resolution Using Very Deep Residual Channel Attention Network (RCAN), is also based on ResNet architecture. MSE loss function ...
Missing: Hourglass | Show results with:Hourglass
Efficient residual attention network for single image super-resolution
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To solve these issues, in this paper, we design a channel hourglass residual structure (CHRS) and explore an efficient channel attention (ECA) mechanism to ...
Feb 22, 2024 · The proposed dual attention mechanism (DAM) integrates multiple receptive fields to compensate for spatial and channel information lost in ...
Jul 10, 2017 · In this paper, we develop an enhanced deep super-resolution network (EDSR) with performance exceeding those of current state-of-the-art SR ...
Missing: Channel Hourglass