43 research outputs found
DeepSUM++: Non-local Deep Neural Network for Super-Resolution of Unregistered Multitemporal Images
Deep learning methods for super-resolution of a remote sensing scene from
multiple unregistered low-resolution images have recently gained attention
thanks to a challenge proposed by the European Space Agency. This paper
presents an evolution of the winner of the challenge, showing how incorporating
non-local information in a convolutional neural network allows to exploit
self-similar patterns that provide enhanced regularization of the
super-resolution problem. Experiments on the dataset of the challenge show
improved performance over the state-of-the-art, which does not exploit
non-local information.Comment: arXiv admin note: text overlap with arXiv:1907.0649
SATVSR: Scenario Adaptive Transformer for Cross Scenarios Video Super-Resolution
Video Super-Resolution (VSR) aims to recover sequences of high-resolution
(HR) frames from low-resolution (LR) frames. Previous methods mainly utilize
temporally adjacent frames to assist the reconstruction of target frames.
However, in the real world, there is a lot of irrelevant information in
adjacent frames of videos with fast scene switching, these VSR methods cannot
adaptively distinguish and select useful information. In contrast, with a
transformer structure suitable for temporal tasks, we devise a novel adaptive
scenario video super-resolution method. Specifically, we use optical flow to
label the patches in each video frame, only calculate the attention of patches
with the same label. Then select the most relevant label among them to
supplement the spatial-temporal information of the target frame. This design
can directly make the supplementary information come from the same scene as
much as possible. We further propose a cross-scale feature aggregation module
to better handle the scale variation problem. Compared with other video
super-resolution methods, our method not only achieves significant performance
gains on single-scene videos but also has better robustness on cross-scene
datasets
An overview of video super-resolution algorithms
We investigate some excellent algorithms in the field of video space super-resolution based on artificial intelligence, structurally analyze the network structure of the algorithm and the commonly used loss functions. We also analyze the characteristics of algorithms in the new field of video space-time super-resolution. This work helps researchers to deeply understand the video super-resolution technology based on artificial intelligence
Temporally Consistent Edge-Informed Video Super-Resolution (Edge-VSR)
Resolution enhancement of a given video sequence is known as video super-resolution. We propose an end-to-end trainable video super-resolution method as an extension of the recently developed edge-informed single image super-resolution algorithm. A two-stage adversarial-based convolutional neural network that incorporates temporal information along with the current frame's structural information will be used. The edge information in each frame along with optical flow technique for motion estimation among frames will be applied. Promising results on validation datasets will be presented
JSI-GAN: GAN-Based Joint Super-Resolution and Inverse Tone-Mapping with Pixel-Wise Task-Specific Filters for UHD HDR Video
Joint learning of super-resolution (SR) and inverse tone-mapping (ITM) has
been explored recently, to convert legacy low resolution (LR) standard dynamic
range (SDR) videos to high resolution (HR) high dynamic range (HDR) videos for
the growing need of UHD HDR TV/broadcasting applications. However, previous
CNN-based methods directly reconstruct the HR HDR frames from LR SDR frames,
and are only trained with a simple L2 loss. In this paper, we take a
divide-and-conquer approach in designing a novel GAN-based joint SR-ITM
network, called JSI-GAN, which is composed of three task-specific subnets: an
image reconstruction subnet, a detail restoration (DR) subnet and a local
contrast enhancement (LCE) subnet. We delicately design these subnets so that
they are appropriately trained for the intended purpose, learning a pair of
pixel-wise 1D separable filters via the DR subnet for detail restoration and a
pixel-wise 2D local filter by the LCE subnet for contrast enhancement.
Moreover, to train the JSI-GAN effectively, we propose a novel detail GAN loss
alongside the conventional GAN loss, which helps enhancing both local details
and contrasts to reconstruct high quality HR HDR results. When all subnets are
jointly trained well, the predicted HR HDR results of higher quality are
obtained with at least 0.41 dB gain in PSNR over those generated by the
previous methods.Comment: The first two authors contributed equally to this work. Accepted at
AAAI 2020. (Camera-ready version