135 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
Deep Graph Laplacian Regularization for Robust Denoising of Real Images
Recent developments in deep learning have revolutionized the paradigm of
image restoration. However, its applications on real image denoising are still
limited, due to its sensitivity to training data and the complex nature of real
image noise. In this work, we combine the robustness merit of model-based
approaches and the learning power of data-driven approaches for real image
denoising. Specifically, by integrating graph Laplacian regularization as a
trainable module into a deep learning framework, we are less susceptible to
overfitting than pure CNN-based approaches, achieving higher robustness to
small datasets and cross-domain denoising. First, a sparse neighborhood graph
is built from the output of a convolutional neural network (CNN). Then the
image is restored by solving an unconstrained quadratic programming problem,
using a corresponding graph Laplacian regularizer as a prior term. The proposed
restoration pipeline is fully differentiable and hence can be end-to-end
trained. Experimental results demonstrate that our work is less prone to
overfitting given small training data. It is also endowed with strong
cross-domain generalization power, outperforming the state-of-the-art
approaches by a remarkable margin
Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy
Generative adversarial networks (GANs) have been extensively studied in the
past few years. Arguably their most significant impact has been in the area of
computer vision where great advances have been made in challenges such as
plausible image generation, image-to-image translation, facial attribute
manipulation and similar domains. Despite the significant successes achieved to
date, applying GANs to real-world problems still poses significant challenges,
three of which we focus on here. These are: (1) the generation of high quality
images, (2) diversity of image generation, and (3) stable training. Focusing on
the degree to which popular GAN technologies have made progress against these
challenges, we provide a detailed review of the state of the art in GAN-related
research in the published scientific literature. We further structure this
review through a convenient taxonomy we have adopted based on variations in GAN
architectures and loss functions. While several reviews for GANs have been
presented to date, none have considered the status of this field based on their
progress towards addressing practical challenges relevant to computer vision.
Accordingly, we review and critically discuss the most popular
architecture-variant, and loss-variant GANs, for tackling these challenges. Our
objective is to provide an overview as well as a critical analysis of the
status of GAN research in terms of relevant progress towards important computer
vision application requirements. As we do this we also discuss the most
compelling applications in computer vision in which GANs have demonstrated
considerable success along with some suggestions for future research
directions. Code related to GAN-variants studied in this work is summarized on
https://github.com/sheqi/GAN_Review.Comment: Accepted by ACM Computing Surveys, 23 November 202
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