Single image super-resolution via blind blurring estimation and dictionary learning

Zhao, Xiaole; Wu, Yue; Tian, Jinsha; Zhang, Hongying

doi:10.1016/j.neucom.2016.02.079

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…CUT provided a general framework for many studies 23 – 28 In our study, we attempted to use self-supervised contrastive learning to enable a structural representation learning network to learn task-specific image structural properties. Instead of using patches directly as the basic token of contrastive learning, as CUT does, we use spatial correlation maps constructed at the patch level.…”

Section: Related Workmentioning

confidence: 99%

Thermal infrared image colorization on nighttime driving scenes with spatially-correlated learning

Wu,

Shi,

Lin

et al. 2024

J. Electron. Imag.

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Despite recent promising advances in thermal infrared image colorization for nighttime driving scenes, the content structure preservation and fidelity of colorized images remain ineffectively addressed. Therefore, we introduce a dynamic spatial structure guidance to automatically learn the task-oriented structure representation in the unpaired thermal infrared image colorization of nighttime driving scenes. First, patch-based spatial correlation maps are constructed for the nighttime thermal infrared image and the color image. The content structure preservation during thermal infrared image colorization is realized by reflecting the patch-based spatial structural correlations between the input and output images. To flexibly utilize the spatial correlations, structural representation learning was constructed to learn task-specific structural characterization. We innovatively treat the spatial structural similarity between the corresponding image patches as an interest metric to guide contrastive learning to optimize structural representation learning. Compared with state-of-theart methods, our model achieved an advanced thermal infrared image colorization performance. Additionally, we performed semantic segmentation and object detection tasks on the colorization to further address the practicality. Extensive qualitative and quantitative experiments and evaluations demonstrate that our method achieves state-of-the-art results in the nighttime thermal infrared colorization.

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Section: Related Workmentioning

confidence: 99%

Thermal infrared image colorization on nighttime driving scenes with spatially-correlated learning

Wu,

Shi,

Lin

et al. 2024

J. Electron. Imag.

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“…This type of approach was justified by the authors since materials have a 3D structure but most of the analysis on image processing that has been done is of 2D images [21]. Image super-resolution (SR) is another interesting application of ML concepts for image processing challenges that has attracted some attention in the past decades [15,42]. In 2016, Zhao et al [42] proposed a framework for single-image super-resolution tasks, consisting of kernel blur estimation, to improve the training quality as well as the model performance.…”

Section: Domainsmentioning

confidence: 99%

“…Image super-resolution (SR) is another interesting application of ML concepts for image processing challenges that has attracted some attention in the past decades [15,42]. In 2016, Zhao et al [42] proposed a framework for single-image super-resolution tasks, consisting of kernel blur estimation, to improve the training quality as well as the model performance. Using the kernel blur estimation, the authors adopted a selective patch processing strategy combined with sparse recovery.…”

Section: Domainsmentioning

confidence: 99%

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Developments in Image Processing Using Deep Learning and Reinforcement Learning

Valente,

António,

Mora

et al. 2023

J. Imaging

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The growth in the volume of data generated, consumed, and stored, which is estimated to exceed 180 zettabytes in 2025, represents a major challenge both for organizations and for society in general. In addition to being larger, datasets are increasingly complex, bringing new theoretical and computational challenges. Alongside this evolution, data science tools have exploded in popularity over the past two decades due to their myriad of applications when dealing with complex data, their high accuracy, flexible customization, and excellent adaptability. When it comes to images, data analysis presents additional challenges because as the quality of an image increases, which is desirable, so does the volume of data to be processed. Although classic machine learning (ML) techniques are still widely used in different research fields and industries, there has been great interest from the scientific community in the development of new artificial intelligence (AI) techniques. The resurgence of neural networks has boosted remarkable advances in areas such as the understanding and processing of images. In this study, we conducted a comprehensive survey regarding advances in AI design and the optimization solutions proposed to deal with image processing challenges. Despite the good results that have been achieved, there are still many challenges to face in this field of study. In this work, we discuss the main and more recent improvements, applications, and developments when targeting image processing applications, and we propose future research directions in this field of constant and fast evolution.

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Blind single image super-resolution with a mixture of deep networks

Wang

et al. 2020

Pattern Recognition

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Single image super-resolution via blind blurring estimation and dictionary learning

Cited by 4 publications

References 26 publications

Thermal infrared image colorization on nighttime driving scenes with spatially-correlated learning

Thermal infrared image colorization on nighttime driving scenes with spatially-correlated learning

Developments in Image Processing Using Deep Learning and Reinforcement Learning

Blind single image super-resolution with a mixture of deep networks

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