34,089 research outputs found
Deep Blind Super-Resolution for Satellite Video
Recent efforts have witnessed remarkable progress in Satellite Video
Super-Resolution (SVSR). However, most SVSR methods usually assume the
degradation is fixed and known, e.g., bicubic downsampling, which makes them
vulnerable in real-world scenes with multiple and unknown degradations. To
alleviate this issue, blind SR has thus become a research hotspot.
Nevertheless, existing approaches are mainly engaged in blur kernel estimation
while losing sight of another critical aspect for VSR tasks: temporal
compensation, especially compensating for blurry and smooth pixels with vital
sharpness from severely degraded satellite videos. Therefore, this paper
proposes a practical Blind SVSR algorithm (BSVSR) to explore more sharp cues by
considering the pixel-wise blur levels in a coarse-to-fine manner.
Specifically, we employed multi-scale deformable convolution to coarsely
aggregate the temporal redundancy into adjacent frames by window-slid
progressive fusion. Then the adjacent features are finely merged into
mid-feature using deformable attention, which measures the blur levels of
pixels and assigns more weights to the informative pixels, thus inspiring the
representation of sharpness. Moreover, we devise a pyramid spatial
transformation module to adjust the solution space of sharp mid-feature,
resulting in flexible feature adaptation in multi-level domains. Quantitative
and qualitative evaluations on both simulated and real-world satellite videos
demonstrate that our BSVSR performs favorably against state-of-the-art
non-blind and blind SR models. Code will be available at
https://github.com/XY-boy/Blind-Satellite-VSRComment: Published in IEEE TGR
UG^2: a Video Benchmark for Assessing the Impact of Image Restoration and Enhancement on Automatic Visual Recognition
Advances in image restoration and enhancement techniques have led to
discussion about how such algorithmscan be applied as a pre-processing step to
improve automatic visual recognition. In principle, techniques like deblurring
and super-resolution should yield improvements by de-emphasizing noise and
increasing signal in an input image. But the historically divergent goals of
the computational photography and visual recognition communities have created a
significant need for more work in this direction. To facilitate new research,
we introduce a new benchmark dataset called UG^2, which contains three
difficult real-world scenarios: uncontrolled videos taken by UAVs and manned
gliders, as well as controlled videos taken on the ground. Over 160,000
annotated frames forhundreds of ImageNet classes are available, which are used
for baseline experiments that assess the impact of known and unknown image
artifacts and other conditions on common deep learning-based object
classification approaches. Further, current image restoration and enhancement
techniques are evaluated by determining whether or not theyimprove baseline
classification performance. Results showthat there is plenty of room for
algorithmic innovation, making this dataset a useful tool going forward.Comment: Supplemental material: https://goo.gl/vVM1xe, Dataset:
https://goo.gl/AjA6En, CVPR 2018 Prize Challenge: ug2challenge.or
Non-blind Image Restoration Based on Convolutional Neural Network
Blind image restoration processors based on convolutional neural network
(CNN) are intensively researched because of their high performance. However,
they are too sensitive to the perturbation of the degradation model. They
easily fail to restore the image whose degradation model is slightly different
from the trained degradation model. In this paper, we propose a non-blind
CNN-based image restoration processor, aiming to be robust against a
perturbation of the degradation model compared to the blind restoration
processor. Experimental comparisons demonstrate that the proposed non-blind
CNN-based image restoration processor can robustly restore images compared to
existing blind CNN-based image restoration processors.Comment: Accepted by IEEE 7th Global Conference on Consumer Electronics, 201
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