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27
Fast Deconvolution-Based Image Super-Resolution Using Gradient Prior
"... Abstract—Single-image super-resolution (SR) is to reconstruct a high-resolution image from a low-resolution input image. Nevertheless, most SR algorithms are performed in an iterative manner and are therefore time-consuming. In this paper, we propose an iteration-free single-image SR algorithm based ..."
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Abstract—Single-image super-resolution (SR) is to reconstruct a high-resolution image from a low-resolution input image. Nevertheless, most SR algorithms are performed in an iterative manner and are therefore time-consuming. In this paper, we propose an iteration-free single-image SR algorithm
Temporally Coherent Superresolution of Textured Video via Dynamic Texture Synthesis
"... Abstract — This paper addresses the problem of hallucinating the missing high-resolution (HR) details of a low-resolution (LR) video while maintaining the temporal coherence of the recon-structed HR details using dynamic texture synthesis (DTS). Most existing multiframe-based video superresolution ( ..."
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Cited by 1 (1 self)
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Abstract — This paper addresses the problem of hallucinating the missing high-resolution (HR) details of a low-resolution (LR) video while maintaining the temporal coherence of the recon-structed HR details using dynamic texture synthesis (DTS). Most existing multiframe-based video superresolution
STOL: Spatio-Temporal Online Dictionary Learning for low bit-rate video coding
"... To speed up the convergence rate of learning dictionary, this paper proposes a spatio-temporal online dictionary learning (STOL) algorithm to improve the original adaptive regularized dictionary learning with K-SVD. Experiments show the super-resolution reconstruction based on STOL obvi-ously reduce ..."
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Ẑh would be recovered from Ẑl by the learning-based super-resolution reconstruction via sparse representation. In training each series of 2-D subdictionaries, the primitives is of low dimen-sionality. The non-primitive volumes are supposed to be consistent along the motion trajectory with little
for Sparse Translation-Invariant Signals
"... Document Version Early version, also known as pre-print Link to publication from Aalborg University Citation for published version (APA): ..."
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Document Version Early version, also known as pre-print Link to publication from Aalborg University Citation for published version (APA):
1Direction of Arrival Estimation Using Co-prime Arrays: A Super Resolution Viewpoint
"... Abstract—We consider the problem of direction of arrival (DOA) estimation using a newly proposed structure of non-uniform linear arrays, referred to as co-prime arrays, in this paper. By exploiting the second order statistical information of the received signals, co-prime arrays exhibit O(MN) degree ..."
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(MN) degrees of freedom with only M + N sensors. A sparsity based recovery method is proposed to fully utilize these degrees of freedom. Unlike traditional sparse recovery methods, the proposed method is based on the developing theory of super resolution, which considers a continuous range of possible sources
P2c2: Programmable pixel compressive camera for high speed imaging
- Computer Vision and Pattern Recognition, IEEE Computer Society Conference on
, 2011
"... We describe an imaging architecture for compressive video sensing termed programmable pixel compressive camera (P2C2). P2C2 allows us to capture fast phenomena at frame rates higher than the camera sensor. In P2C2, each pixel has an independent shutter that is modulated at a rate higher than the cam ..."
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Cited by 37 (6 self)
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the camera frame-rate. The observed intensity at a pixel is an integration of the incoming light modulated by its specific shutter. We propose a reconstruction algorithm that uses the data from P2C2 along with additional priors about videos to perform temporal superresolution. We model the spatial redundancy
Multiscale Online Dictionary Learning for Quality Scalable Video Coding
"... This paper proposes a novel multiscale online dictionary learning algorithm with double sparsity structure for scalable video coding. Along hierarchical structures on the feature set by wavelet transform, the search space of online learning is optimized to sub-blocks for hierarchical sparsity. The g ..."
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by the scalable super-resolution reconstruction via sparse representation in a SNR-scalable manner. The multiscale super-resolution reconstruction can be defined as an energy minimization: f (αdL,XH) = arg min X,Hlα d
1FlatCam: Thin, Bare-Sensor Cameras using Coded Aperture and Computation
"... FlatCam is a thin form-factor lensless camera that consists of a coded mask placed on top of a bare, conventional sensor array. Unlike a traditional, lens-based camera where an image of the scene is directly recorded on the sensor pixels, each pixel in FlatCam records a linear combination of light f ..."
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from multiple scene elements. A computational algorithm is then used to demultiplex the recorded measurements and reconstruct an image of the scene. FlatCam is an instance of a coded aperture imaging system; however, unlike the vast majority of related work, we place the coded mask extremely close
RICE UNIVERSITY Regime Change: Sampling Rate vs. Bit-Depth in Compressive Sensing
, 2011
"... The compressive sensing (CS) framework aims to ease the burden on analog-to-digital converters (ADCs) by exploiting inherent structure in natural and man-made signals. It has been demon-strated that structured signals can be acquired with just a small number of linear measurements, on the order of t ..."
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. We develop a new theoretical framework to analyze this extreme case and develop new algorithms for signal reconstruction from such coarsely quantized measurements. The 1-bit CS framework leads us to scenarios where it may be more appropriate to reduce bit-depth instead of sampling rate. We find
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