Simultaneous super-resolution and blind deconvolution
In many real applications, blur in input low-resolution images is a nuisance, which prevents traditional super-resolution methods from working correctly. This paper presents a unifying approach to the blind deconvolution and superresolution problem of multiple degraded low-resolution frames of the original scene.
Sroubek, F. +2 more
openaire +2 more sources
A first order phase transition mechanism underlies protein aggregation in mammalian cells [PDF]
The formation of misfolded protein aggregates is a hallmark of neurodegenerative diseases. The aggregate formation process exhibits an initial lag phase when precursor clusters spontaneously assemble.
Abraham +67 more
core +2 more sources
A Progressive Decoupled Network for Blind Image Super-Resolution
Blind super-resolution (Blind SR) has become a popular research topic in computer vision in super-resolution, which aims to enhance low-resolution (LR) images with unknown or partially known degradation blur kernels.
Laigan Luo, Benshun Yi, Chao Zhu
doaj +1 more source
Boosting Degradation Representation Learning for Blind Image Super-Resolution [PDF]
In most convolutional neural networks-based super-resolution (SR) methods, the degradation assumptions are fixed and known (e.g., bicubic degradation). When applying them to real-world degraded images, the mismatch between the actual degradation and the ...
YUAN Jiang, MA Ji, ZHOU Dengwen
doaj +1 more source
Light field super resolution through controlled micro-shifts of light field sensor [PDF]
Light field cameras enable new capabilities, such as post-capture refocusing and aperture control, through capturing directional and spatial distribution of light rays in space.
Gunturk, Bahadir K., Mukati, M. Umair
core +2 more sources
Multiple Frame Splicing and Degradation Learning for Hyperspectral Imagery Super-Resolution
Hyperspectral imagery (HSI) is an emerging remote sensing technology to discriminate different remote sensing objects. However, the HSI spatial resolution is relatively low due to the trade-off in restricted physical hardware and various imaging ...
Chenwei Deng, Xingshi Luo, Wenzheng Wang
doaj +1 more source
Gradient Scan Gibbs Sampler: an efficient algorithm for high-dimensional Gaussian distributions
This paper deals with Gibbs samplers that include high dimensional conditional Gaussian distributions. It proposes an efficient algorithm that avoids the high dimensional Gaussian sampling and relies on a random excursion along a small set of directions.
Féron, Olivier +2 more
core +3 more sources
A sub-Nyquist co-prime sampling music spectral approach for natural frequency identification of white-noise excited structures [PDF]
Motivated by practical needs to reduce data transmission payloads in wireless sensors for vibration-based monitoring of civil engineering structures, this paper proposes a novel approach for identifying resonant frequencies of white-noise excited ...
Giaralis, A., Gkoktsi, K.
core +1 more source
X-ray image separation via coupled dictionary learning [PDF]
In support of art investigation, we propose a new source sepa- ration method that unmixes a single X-ray scan acquired from double-sided paintings. Unlike prior source separation meth- ods, which are based on statistical or structural incoherence of the ...
Cornelis, Bruno +4 more
core +2 more sources
Despite natural image super-resolution (SR) methods have achieved great success, super-resolution methods for hyperspectral image (HSI) with rich spectral features are still a very challenging task.
Lijing Bu +3 more
doaj +1 more source

