106 research outputs found
Graph Spectral Image Processing
Recent advent of graph signal processing (GSP) has spurred intensive studies
of signals that live naturally on irregular data kernels described by graphs
(e.g., social networks, wireless sensor networks). Though a digital image
contains pixels that reside on a regularly sampled 2D grid, if one can design
an appropriate underlying graph connecting pixels with weights that reflect the
image structure, then one can interpret the image (or image patch) as a signal
on a graph, and apply GSP tools for processing and analysis of the signal in
graph spectral domain. In this article, we overview recent graph spectral
techniques in GSP specifically for image / video processing. The topics covered
include image compression, image restoration, image filtering and image
segmentation
GQE-Net: A Graph-based Quality Enhancement Network for Point Cloud Color Attribute
In recent years, point clouds have become increasingly popular for
representing three-dimensional (3D) visual objects and scenes. To efficiently
store and transmit point clouds, compression methods have been developed, but
they often result in a degradation of quality. To reduce color distortion in
point clouds, we propose a graph-based quality enhancement network (GQE-Net)
that uses geometry information as an auxiliary input and graph convolution
blocks to extract local features efficiently. Specifically, we use a
parallel-serial graph attention module with a multi-head graph attention
mechanism to focus on important points or features and help them fuse together.
Additionally, we design a feature refinement module that takes into account the
normals and geometry distance between points. To work within the limitations of
GPU memory capacity, the distorted point cloud is divided into overlap-allowed
3D patches, which are sent to GQE-Net for quality enhancement. To account for
differences in data distribution among different color omponents, three models
are trained for the three color components. Experimental results show that our
method achieves state-of-the-art performance. For example, when implementing
GQE-Net on the recent G-PCC coding standard test model, 0.43 dB, 0.25 dB, and
0.36 dB Bjontegaard delta (BD)-peak-signal-to-noise ratio (PSNR), corresponding
to 14.0%, 9.3%, and 14.5% BD-rate savings can be achieved on dense point clouds
for the Y, Cb, and Cr components, respectively.Comment: 13 pages, 11 figures, submitted to IEEE TI
Recent Advances in Image Restoration with Applications to Real World Problems
In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included
Graph Signal Restoration Using Nested Deep Algorithm Unrolling
Graph signal processing is a ubiquitous task in many applications such as
sensor, social, transportation and brain networks, point cloud processing, and
graph neural networks. Graph signals are often corrupted through sensing
processes, and need to be restored for the above applications. In this paper,
we propose two graph signal restoration methods based on deep algorithm
unrolling (DAU). First, we present a graph signal denoiser by unrolling
iterations of the alternating direction method of multiplier (ADMM). We then
propose a general restoration method for linear degradation by unrolling
iterations of Plug-and-Play ADMM (PnP-ADMM). In the second method, the unrolled
ADMM-based denoiser is incorporated as a submodule. Therefore, our restoration
method has a nested DAU structure. Thanks to DAU, parameters in the proposed
denoising/restoration methods are trainable in an end-to-end manner. Since the
proposed restoration methods are based on iterations of a (convex) optimization
algorithm, the method is interpretable and keeps the number of parameters small
because we only need to tune graph-independent regularization parameters. We
solve two main problems in existing graph signal restoration methods: 1)
limited performance of convex optimization algorithms due to fixed parameters
which are often determined manually. 2) large number of parameters of graph
neural networks that result in difficulty of training. Several experiments for
graph signal denoising and interpolation are performed on synthetic and
real-world data. The proposed methods show performance improvements to several
existing methods in terms of root mean squared error in both tasks
Unrolling of Graph Total Variation for Image Denoising
While deep learning have enabled effective solutions in image denoising, in general their implementations overly rely on training data and require tuning of a large parameter set. In this thesis, a hybrid design that combines graph signal filtering with feature learning is proposed. It utilizes interpretable analytical low-pass graph filters and employs 80\% fewer parameters than a state-of-the-art DL denoising scheme called DnCNN. Specifically, to construct a graph for graph spectral filtering, a CNN is used to learn features per pixel, then feature distances are computed to establish edge weights. Given a constructed graph, a convex optimization problem for denoising using a graph total variation prior is formulated. Its solution is interpreted in an iterative procedure as a graph low-pass filter with an analytical frequency response. For fast implementation, this response is realized by Lanczos approximation. This method outperformed DnCNN by up to 3dB in PSNR in statistical mistmatch case
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