Generalized Dini theorems for nets of functions on arbitrary sets

Timofte, Vlad; Timofte, Aida

doi:10.1007/s11117-015-0348-2

Cited by 8 publications

(4 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For sparse coding [14], the coefficients of low-resolution dictionary are one-to-one correspondence with the coefficients under the high-resolution dictionary. Neighborhood embedding method [16] up-samples LR patches to find similar LR patches under the lowdimensional features, and combines with corresponding HR patches to reconstruct.…”

Section: Non Deep-learning Methodsmentioning

confidence: 99%

A Sensor Image Super-Resolution via Advanced Generative Adversarial Network

Wang¹,

He²,

Du-yan³

et al. 2018

Preprint

View full text Add to dashboard Cite

Complementary Metal-Oxide-Semiconductor (CMOS) is a typical image sensor that has a wide range of applications. However, considering the limitations of the weather condition and hardware cost, it is hard to capture high-resolution images by CMOS sensor. Recently, Super-Resolution (SR) techniques for image restoration has been gaining attentions due to its excellent performance. Under the powerful learning ability, Generative Adversarial Networks (GANs) have been proved to achieve great success. In this paper, we propose the Advanced Generative Adversarial Networks (AGAN) to efficiently correct these issues; 1) we design a Laplacian pyramid framework as pre-trained module, which is beneficial to provide multi-scale features for our input. 2) at each feature block, a convolutional skip-connections network, which may contain some latent information, is significant for generative model to reconstruct a plausible-looking image; 3) considering that edge details usually play an important role in image generation, a novel perceptual loss function is defined to train and seek optimal parameters. It is effective to achieve excellent and compelling quality captured by CMOS sensor. Quantitative and qualitative evaluations have been demonstrated that our algorithm not only fully takes advantage of Convolutional Neural Networks (CNNs) to improve the image quality, but also performs better than previous GAN algorithms for super-resolution task.

show abstract

Section: Non Deep-learning Methodsmentioning

confidence: 99%

A Sensor Image Super-Resolution via Advanced Generative Adversarial Network

Wang¹,

He²,

Du-yan³

et al. 2018

Preprint

View full text Add to dashboard Cite

show abstract

“…Hence the f i 's is a net of monotonically decreasing continuous maps with compact support, converging pointwise to 0. By the generalized Dini's Theorem (see [10], Corollary 6) we have that f i → 0 uniformly.…”

Section: Let Us Prove (3) By Local Compactnessmentioning

confidence: 97%

A Generalization of Renault's Theorem for Cartan Subalgebras

Raad¹

2021

Preprint

View full text Add to dashboard Cite

show abstract

“…But by the Heine-Borel property, B is compact. Now, [5,Corollary 15] implies that T α (B) → 0 in Y . This means that B b c (X, Y ) possesses the Lebesgue property.…”

Section: Proof (I) Assume |T | ≤ |S|mentioning

confidence: 99%

$AM$-spaces from a locally solid vector lattice point of view with applications

Zabeti¹

2019

Preprint

View full text Add to dashboard Cite

Suppose X is a locally solid vector lattice. In this paper, we introduce the notion "AM -property" in X as an extension for AM -spaces in the category of all Banach lattices. With the aid of this concept, we characterize spaces in which bounded sets and order bounded sets agree. This, in turn, characterizes conditions under which each class of bounded operators on X is order bounded and vice versa. Also, we show that under some natural assumptions, different types of bounded order bounded operators on X have the Lebesgue or Levi property if and only if so is X.

show abstract

Generalized Dini theorems for nets of functions on arbitrary sets

Cited by 8 publications

References 8 publications

A Sensor Image Super-Resolution via Advanced Generative Adversarial Network

A Sensor Image Super-Resolution via Advanced Generative Adversarial Network

A Generalization of Renault's Theorem for Cartan Subalgebras

$AM$-spaces from a locally solid vector lattice point of view with applications

Contact Info

Product

Resources

About