9,673 research outputs found
A deep learning framework for quality assessment and restoration in video endoscopy
Endoscopy is a routine imaging technique used for both diagnosis and
minimally invasive surgical treatment. Artifacts such as motion blur, bubbles,
specular reflections, floating objects and pixel saturation impede the visual
interpretation and the automated analysis of endoscopy videos. Given the
widespread use of endoscopy in different clinical applications, we contend that
the robust and reliable identification of such artifacts and the automated
restoration of corrupted video frames is a fundamental medical imaging problem.
Existing state-of-the-art methods only deal with the detection and restoration
of selected artifacts. However, typically endoscopy videos contain numerous
artifacts which motivates to establish a comprehensive solution.
We propose a fully automatic framework that can: 1) detect and classify six
different primary artifacts, 2) provide a quality score for each frame and 3)
restore mildly corrupted frames. To detect different artifacts our framework
exploits fast multi-scale, single stage convolutional neural network detector.
We introduce a quality metric to assess frame quality and predict image
restoration success. Generative adversarial networks with carefully chosen
regularization are finally used to restore corrupted frames.
Our detector yields the highest mean average precision (mAP at 5% threshold)
of 49.0 and the lowest computational time of 88 ms allowing for accurate
real-time processing. Our restoration models for blind deblurring, saturation
correction and inpainting demonstrate significant improvements over previous
methods. On a set of 10 test videos we show that our approach preserves an
average of 68.7% which is 25% more frames than that retained from the raw
videos.Comment: 14 page
Learning to Predict Image-based Rendering Artifacts with Respect to a Hidden Reference Image
Image metrics predict the perceived per-pixel difference between a reference
image and its degraded (e. g., re-rendered) version. In several important
applications, the reference image is not available and image metrics cannot be
applied. We devise a neural network architecture and training procedure that
allows predicting the MSE, SSIM or VGG16 image difference from the distorted
image alone while the reference is not observed. This is enabled by two
insights: The first is to inject sufficiently many un-distorted natural image
patches, which can be found in arbitrary amounts and are known to have no
perceivable difference to themselves. This avoids false positives. The second
is to balance the learning, where it is carefully made sure that all image
errors are equally likely, avoiding false negatives. Surprisingly, we observe,
that the resulting no-reference metric, subjectively, can even perform better
than the reference-based one, as it had to become robust against
mis-alignments. We evaluate the effectiveness of our approach in an image-based
rendering context, both quantitatively and qualitatively. Finally, we
demonstrate two applications which reduce light field capture time and provide
guidance for interactive depth adjustment.Comment: 13 pages, 11 figure
Deep Neural Networks for No-Reference and Full-Reference Image Quality Assessment
We present a deep neural network-based approach to image quality assessment
(IQA). The network is trained end-to-end and comprises ten convolutional layers
and five pooling layers for feature extraction, and two fully connected layers
for regression, which makes it significantly deeper than related IQA models.
Unique features of the proposed architecture are that: 1) with slight
adaptations it can be used in a no-reference (NR) as well as in a
full-reference (FR) IQA setting and 2) it allows for joint learning of local
quality and local weights, i.e., relative importance of local quality to the
global quality estimate, in an unified framework. Our approach is purely
data-driven and does not rely on hand-crafted features or other types of prior
domain knowledge about the human visual system or image statistics. We evaluate
the proposed approach on the LIVE, CISQ, and TID2013 databases as well as the
LIVE In the wild image quality challenge database and show superior performance
to state-of-the-art NR and FR IQA methods. Finally, cross-database evaluation
shows a high ability to generalize between different databases, indicating a
high robustness of the learned features
Bridge the Gap Between VQA and Human Behavior on Omnidirectional Video: A Large-Scale Dataset and a Deep Learning Model
Omnidirectional video enables spherical stimuli with the viewing range. Meanwhile, only the viewport region of omnidirectional
video can be seen by the observer through head movement (HM), and an even
smaller region within the viewport can be clearly perceived through eye
movement (EM). Thus, the subjective quality of omnidirectional video may be
correlated with HM and EM of human behavior. To fill in the gap between
subjective quality and human behavior, this paper proposes a large-scale visual
quality assessment (VQA) dataset of omnidirectional video, called VQA-OV, which
collects 60 reference sequences and 540 impaired sequences. Our VQA-OV dataset
provides not only the subjective quality scores of sequences but also the HM
and EM data of subjects. By mining our dataset, we find that the subjective
quality of omnidirectional video is indeed related to HM and EM. Hence, we
develop a deep learning model, which embeds HM and EM, for objective VQA on
omnidirectional video. Experimental results show that our model significantly
improves the state-of-the-art performance of VQA on omnidirectional video.Comment: Accepted by ACM MM 201
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