Discrete cosine transform network for guided depth map super-resolution
Guided depth super-resolution (GDSR) is an essential topic in multi-modal image
processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones …
processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones …
Context reasoning attention network for image super-resolution
Deep convolutional neural networks (CNNs) are achieving great successes for image super-
resolution (SR), where global context is crucial for accurate restoration. However, the basic …
resolution (SR), where global context is crucial for accurate restoration. However, the basic …
[HTML][HTML] Countering malicious deepfakes: Survey, battleground, and horizon
The creation or manipulation of facial appearance through deep generative approaches,
known as DeepFake, have achieved significant progress and promoted a wide range of …
known as DeepFake, have achieved significant progress and promoted a wide range of …
Dynamic high-pass filtering and multi-spectral attention for image super-resolution
Deep convolutional neural networks (CNNs) have pushed forward the frontier of super-
resolution (SR) research. However, current CNN models exhibit a major flaw: they are …
resolution (SR) research. However, current CNN models exhibit a major flaw: they are …
Learning discriminative feature representation with pixel-level supervision for forest smoke recognition
H Tao, Q Duan, M Lu, Z Hu - Pattern Recognition, 2023 - Elsevier
Existing vision-based smoke recognition methods still face the issues of low detection rates
and high false alarm rates in complex scenes. One reason is that they label light smoke and …
and high false alarm rates in complex scenes. One reason is that they label light smoke and …
[HTML][HTML] A review of image super-resolution approaches based on deep learning and applications in remote sensing
At present, with the advance of satellite image processing technology, remote sensing
images are becoming more widely used in real scenes. However, due to the limitations of …
images are becoming more widely used in real scenes. However, due to the limitations of …
Cross view capture for stereo image super-resolution
Stereo image super-resolution exploits additional features from cross view image pairs for
high resolution (HR) image reconstruction. Recently, several new methods have been …
high resolution (HR) image reconstruction. Recently, several new methods have been …
Single image super-resolution based on directional variance attention network
Recent advances in single image super-resolution (SISR) explore the power of deep
convolutional neural networks (CNNs) to achieve better performance. However, most of the …
convolutional neural networks (CNNs) to achieve better performance. However, most of the …
Aim 2020 challenge on efficient super-resolution: Methods and results
This paper reviews the AIM 2020 challenge on efficient single image super-resolution with
focus on the proposed solutions and results. The challenge task was to super-resolve an …
focus on the proposed solutions and results. The challenge task was to super-resolve an …
Aligned structured sparsity learning for efficient image super-resolution
Lightweight image super-resolution (SR) networks have obtained promising results with
moderate model size. Many SR methods have focused on designing lightweight …
moderate model size. Many SR methods have focused on designing lightweight …