1,896 research outputs found
Solving Inverse Problems with Piecewise Linear Estimators: From Gaussian Mixture Models to Structured Sparsity
A general framework for solving image inverse problems is introduced in this
paper. The approach is based on Gaussian mixture models, estimated via a
computationally efficient MAP-EM algorithm. A dual mathematical interpretation
of the proposed framework with structured sparse estimation is described, which
shows that the resulting piecewise linear estimate stabilizes the estimation
when compared to traditional sparse inverse problem techniques. This
interpretation also suggests an effective dictionary motivated initialization
for the MAP-EM algorithm. We demonstrate that in a number of image inverse
problems, including inpainting, zooming, and deblurring, the same algorithm
produces either equal, often significantly better, or very small margin worse
results than the best published ones, at a lower computational cost.Comment: 30 page
Comparison of super-resolution algorithms applied to retinal images
A critical challenge in biomedical imaging is to optimally balance the trade-off among image resolution, signal-to-noise ratio, and acquisition time. Acquiring a high-resolution image is possible; however, it is either expensive or time consuming or both. Resolution is also limited by the physical properties of the imaging device, such as the nature and size of the input source radiation and the optics of the device. Super-resolution (SR), which is an off-line approach for improving the resolution of an image, is free of these trade-offs. Several methodologies, such as interpolation, frequency domain, regularization, and learning-based approaches, have been developed over the past several years for SR of natural images. We review some of these methods and demonstrate the positive impact expected from SR of retinal images and investigate the performance of various SR techniques. We use a fundus image as an example for simulations
Deep Learning for Single Image Super-Resolution: A Brief Review
Single image super-resolution (SISR) is a notoriously challenging ill-posed
problem, which aims to obtain a high-resolution (HR) output from one of its
low-resolution (LR) versions. To solve the SISR problem, recently powerful deep
learning algorithms have been employed and achieved the state-of-the-art
performance. In this survey, we review representative deep learning-based SISR
methods, and group them into two categories according to their major
contributions to two essential aspects of SISR: the exploration of efficient
neural network architectures for SISR, and the development of effective
optimization objectives for deep SISR learning. For each category, a baseline
is firstly established and several critical limitations of the baseline are
summarized. Then representative works on overcoming these limitations are
presented based on their original contents as well as our critical
understandings and analyses, and relevant comparisons are conducted from a
variety of perspectives. Finally we conclude this review with some vital
current challenges and future trends in SISR leveraging deep learning
algorithms.Comment: Accepted by IEEE Transactions on Multimedia (TMM
Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement
In this paper, we propose a Hierarchical Learned Video Compression (HLVC)
method with three hierarchical quality layers and a recurrent enhancement
network. The frames in the first layer are compressed by an image compression
method with the highest quality. Using these frames as references, we propose
the Bi-Directional Deep Compression (BDDC) network to compress the second layer
with relatively high quality. Then, the third layer frames are compressed with
the lowest quality, by the proposed Single Motion Deep Compression (SMDC)
network, which adopts a single motion map to estimate the motions of multiple
frames, thus saving bits for motion information. In our deep decoder, we
develop the Weighted Recurrent Quality Enhancement (WRQE) network, which takes
both compressed frames and the bit stream as inputs. In the recurrent cell of
WRQE, the memory and update signal are weighted by quality features to
reasonably leverage multi-frame information for enhancement. In our HLVC
approach, the hierarchical quality benefits the coding efficiency, since the
high quality information facilitates the compression and enhancement of low
quality frames at encoder and decoder sides, respectively. Finally, the
experiments validate that our HLVC approach advances the state-of-the-art of
deep video compression methods, and outperforms the "Low-Delay P (LDP) very
fast" mode of x265 in terms of both PSNR and MS-SSIM. The project page is at
https://github.com/RenYang-home/HLVC.Comment: Published in CVPR 2020; corrected a minor typo in the footnote of
Table 1; corrected Figure 1
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
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