314 research outputs found
A Deep Learning Approach to Denoise Optical Coherence Tomography Images of the Optic Nerve Head
Purpose: To develop a deep learning approach to de-noise optical coherence
tomography (OCT) B-scans of the optic nerve head (ONH).
Methods: Volume scans consisting of 97 horizontal B-scans were acquired
through the center of the ONH using a commercial OCT device (Spectralis) for
both eyes of 20 subjects. For each eye, single-frame (without signal
averaging), and multi-frame (75x signal averaging) volume scans were obtained.
A custom deep learning network was then designed and trained with 2,328 "clean
B-scans" (multi-frame B-scans), and their corresponding "noisy B-scans" (clean
B-scans + gaussian noise) to de-noise the single-frame B-scans. The performance
of the de-noising algorithm was assessed qualitatively, and quantitatively on
1,552 B-scans using the signal to noise ratio (SNR), contrast to noise ratio
(CNR), and mean structural similarity index metrics (MSSIM).
Results: The proposed algorithm successfully denoised unseen single-frame OCT
B-scans. The denoised B-scans were qualitatively similar to their corresponding
multi-frame B-scans, with enhanced visibility of the ONH tissues. The mean SNR
increased from dB (single-frame) to dB
(denoised). For all the ONH tissues, the mean CNR increased from (single-frame) to (denoised). The MSSIM increased from
(single frame) to (denoised) when compared with
the corresponding multi-frame B-scans.
Conclusions: Our deep learning algorithm can denoise a single-frame OCT
B-scan of the ONH in under 20 ms, thus offering a framework to obtain superior
quality OCT B-scans with reduced scanning times and minimal patient discomfort
A Comprehensive Review of Deep Learning-based Single Image Super-resolution
Image super-resolution (SR) is one of the vital image processing methods that
improve the resolution of an image in the field of computer vision. In the last
two decades, significant progress has been made in the field of
super-resolution, especially by utilizing deep learning methods. This survey is
an effort to provide a detailed survey of recent progress in single-image
super-resolution in the perspective of deep learning while also informing about
the initial classical methods used for image super-resolution. The survey
classifies the image SR methods into four categories, i.e., classical methods,
supervised learning-based methods, unsupervised learning-based methods, and
domain-specific SR methods. We also introduce the problem of SR to provide
intuition about image quality metrics, available reference datasets, and SR
challenges. Deep learning-based approaches of SR are evaluated using a
reference dataset. Some of the reviewed state-of-the-art image SR methods
include the enhanced deep SR network (EDSR), cycle-in-cycle GAN (CinCGAN),
multiscale residual network (MSRN), meta residual dense network (Meta-RDN),
recurrent back-projection network (RBPN), second-order attention network (SAN),
SR feedback network (SRFBN) and the wavelet-based residual attention network
(WRAN). Finally, this survey is concluded with future directions and trends in
SR and open problems in SR to be addressed by the researchers.Comment: 56 Pages, 11 Figures, 5 Table
Image-guided ToF depth upsampling: a survey
Recently, there has been remarkable growth of interest in the development and applications of time-of-flight (ToF) depth cameras. Despite the permanent improvement of their characteristics, the practical applicability of ToF cameras is still limited by low resolution and quality of depth measurements. This has motivated many researchers to combine ToF cameras with other sensors in order to enhance and upsample depth images. In this paper, we review the approaches that couple ToF depth images with high-resolution optical images. Other classes of upsampling methods are also briefly discussed. Finally, we provide an overview of performance evaluation tests presented in the related studies
Rich probabilistic models for semantic labeling
Das Ziel dieser Monographie ist es die Methoden und Anwendungen des semantischen Labelings zu erforschen. Unsere Beiträge zu diesem sich rasch entwickelten Thema sind bestimmte Aspekte der Modellierung und der Inferenz in probabilistischen Modellen und ihre Anwendungen in den interdisziplinären Bereichen der Computer Vision sowie medizinischer Bildverarbeitung und Fernerkundung
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