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SR-AFU: super-resolution network using adaptive frequency component upsampling and multi-resolution features
Image super-resolution (SR) is one of the classic computer vision tasks. This paper proposes a super-resolution network based on adaptive frequency...
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MFFN: image super-resolution via multi-level features fusion network
Deep convolutional neural networks can effectively improve the performance of single-image super-resolution reconstruction. Deep networks tend to...
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Enhanced multi-level features for very high resolution remote sensing scene classification
Very high resolution (VHR) remote sensing (RS) scene classification is a challenging task due to the higher inter-class similarity and intra-class...
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Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE)—A Novel DL Model for Image Multi-resolution
In this paper, we design and evaluate the performance of the Multi-resolution Twinned Residual Auto-Encoders (MR-TRAE) model, a deep learning...
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MadFormer: multi-attention-driven image super-resolution method based on Transformer
While the Transformer-based method has demonstrated exceptional performance in low-level visual processing tasks, it has a strong modeling ability...
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Multi-feature self-attention super-resolution network
In recent years, single-image super-resolution (SISR) methods based on the attention mechanism have been widely explored and achieved remarkable...
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MFMANet: a multispectral pedestrian detection network using multi-resolution RGB feature reuse with multi-scale FIR attentions
In the realm of multispectral pedestrian detection, especially under challenging low-illumination, the existing methods, characterized by...
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Bidirectional Multi-scale Deformable Attention for Video Super-Resolution
Video super-resolution aims to generate a high-resolution video frame from its low-resolution video sequences. Video super-resolution is still a...
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Multi-scale cross-fusion for arbitrary scale image super resolution
Deep convolutional neural networks (CNNs) have great improvements for single image super resolution (SISR). However, most of the existing SISR...
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Multi-level continuous encoding and decoding based on dilation convolution for super-resolution
Deep neural networks have shown better effects for super-resolution in recent years. However, it is difficult to extract multi-level features of...
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Residual multi-branch distillation network for efficient image super-resolution
A Residual Multi-branch Distillation Network (RMDN) is proposed and implemented for efficient image super-resolution (SR) by reducing the...
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Multi-resolution feature perception network for UAV person re-identification
Person re-identification (re-id) with unmanned aerial vehicles (UAVs) is of great significance in intelligent surveillance. However, recognizing a...
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Multi-scale gated network for efficient image super-resolution
Remarkable progress has been made in the field of single-image super-resolution (SISR), with convolutional neural network being widely adopted to...
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Multi-orientation depthwise extraction for stereo image super-resolution
The increasing trend of binocular imaging in recent years has sparked a surge of interest in stereo image super-resolution. While considerable...
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CA-DBMNet: a channel attention based dual branch multi-scale network for depth map super-resolution
Scene depth information plays a fundamental role and can be beneficial to various computer vision or visual robotics applications. The scene color...
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MS-HRNet: multi-scale high-resolution network for human pose estimation
Human pose estimation has important applications in medical diagnosis (such as early diagnosis of autism in children and assisting with the diagnosis...
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Lightweight image super-resolution via multi-branch aware CNN and efficient transformer
A hybrid architecture model of multi-branch aware CNN and efficient transformer (MAET) is proposed and implemented for lightweight image...
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Scene text image super-resolution using multi-scale convolutional neural network with skip connections
Scene text image super-resolution is an interesting and challenging task which aims to enhance the spatial resolution of low-resolution text images...
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Recurrent auto-encoder with multi-resolution ensemble and predictive coding for multivariate time-series anomaly detection
As large-scale time-series data can easily be found in real-world applications, multivariate time-series anomaly detection has played an essential...
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CoT-MISR:Marrying convolution and transformer for multi-image super-resolution
Image super-resolution, a technique for image restoration, has been the subject of extensive research. The challenge lies in converting a...
