Image super-resolution
TL;DR: This paper aims to provide a review of SR from the perspective of techniques and applications, and especially the main contributions in recent years, and discusses the current obstacles for future research.
About: This article is published in Signal Processing.The article was published on 2016-11-01. It has received 378 citations till now.
Citations
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01 Nov 2018
TL;DR: This paper proposes to train a set of novel multi-pairs of dictionaries for different categories of patches which clustered by gaussian mixture model, instead of a global dictionary trained from all patches.
Abstract: Image super-resolution based on learning dictionary has recently attracted enormous interests. The learning-based methods usually train a pair of dictionaries from low-resolution and high-resolution image patches, ignoring the fact that patches have different structures. In this paper, we propose to train a set of novel multi-pairs of dictionaries for different categories of patches which clustered by gaussian mixture model, instead of a global dictionary trained from all patches. The multi-pairs of dictionaries via patch prior guided clustering can express structure information of the image patches well. Extensive experimental results prove it has strong robustness in super resolution. Compared with state-of-the-art SR methods, our method demonstrates more pleasant quality of image edge structures and texture.
1 citations
Cites background from "Image super-resolution"
...[1] Lin wei Yue, Huanfeng Shen, Jie Li, Image super-resolution the techniques, applications, and the future, Signal Processing....
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TL;DR: Zhang et al. as mentioned in this paper proposed an edge-guided video SR (EGVSR) framework for video satellite image SR, which couples the multiframe SR model and the edge-SFSR model in a unified network.
Abstract: Image super-resolution (SR) is an effective solution to the limitation of the spatial resolution of video satellite images, which is caused by the degradation and compression in the imaging phase. For the processing of satellite videos, the commonly employed deep-learning-based single-frame SR (SFSR) framework has limited performance without using complementary information between the video frames. On the other side, the multiframe SR (MFSR) can utilize temporal subpixel information to super-resolve the high-resolution (HR) imagery. However, although deeper and wider deep learning network provides powerful feature representations for SR methods, it has always been a challenge to accurately reconstruct the boundaries of ground objects in video satellite images. In this article, to address these issues, we propose an edge-guided video SR (EGVSR) framework for video satellite image SR, which couples the MFSR model and the edge-SFSR (E-SFSR) model in a unified network. The EGVSR framework is composed of an MFSR branch and an edge branch. The MFSR branch is used to extract the complementary features from the consecutive video frames. Concurrently, the edge branch acts as an SFSR model to translate the edge maps from the low-resolution modality to the HR one. At the final SR stage, the DBFM is built to focus on the promising inner representations of the features of the two branches and fuse them. Extensive experiments on video satellite imagery show that the proposed EGVSR method can achieve superior performance compared to the representative deep-learning-based SR methods.
1 citations
TL;DR: This paper designs a dual stream network to jointly explore the textural and structural information for quality prediction, dubbed TSNet, and develops the spatial attention mechanism to make the visual-sensitive areas more distinguishable, which improves the prediction accuracy.
Abstract: —Image super-resolution (SR) has been widely in- vestigated in recent years. However, it is challenging to fairly estimate the performances of various SR methods, as the lack of reliable and accurate criteria for perceptual quality. Existing SR image quality assessment (IQA) metrics usually concentrate on the specific kind of degradation without distinguishing the visual sensitive areas, which have no adaptive ability to describe the diverse SR degeneration situations. In this paper, we focus on the textural and structural degradation of image SR which acts as a critical role for visual perception, and design a dual stream network to jointly explore the textural and structural information for quality prediction, dubbed TSNet. By mimicking the human vision system (HVS) that pays more attention to the significant areas of the image, we develop the spatial attention mechanism to make the visual-sensitive areas more distinguishable, which improves the prediction accuracy. Feature normalization (F-Norm) is also developed to investigate the inherent spatial correlation of SR features and boost the network representation capacity. Experimental results show the proposed TSNet predicts the visual quality more accurate than the state-of-the-art IQA methods, and demonstrates better consistency with the human’s perspective. The source code will be made available at http://github.com/yuqing-liu-dut/NRIQA SR.
1 citations
TL;DR: In this article , a termal kameradan elde edilirken düşük kalitede bulanık görüntüler meydana gelebilmektedir.
Abstract: Termal kamera sistemleri, ısı değişiminin tespitini gerektiren her türlü uygulamada faydalanılabilmesine rağmen termal görüntüleme sistemleri oldukça yüksek maliyete sahip sistemlerdir ve bu durum termal sistemlerin yaygın bir şekilde kullanımını zorlaştırmaktadır. Ayrıca termal görüntüler elde edilirken düşük kalitede bulanık görüntüler meydana gelebilmektedir. Bu makalede, iki farklı termal kameradan elde edilen termal yüz görüntülerinden oluşan bir veri seti üzerinde süper çözünürlük uygulaması gerçekleştirilmiştir. Belirtilen veri seti geleneksel yöntemlerden farklı bir şekilde oluşturulmuş olup, düşük çözünürlüklü (LR) termal görüntüler 160x120 termal çözünürlüğe sahip kameradan elde edilirken yüksek çözünürlüklü(referans) görüntüler ise 640x480 termal çözünürlüğe sahip kameradan elde edilmiştir. Daha sonra bu görüntülerdeki gereksiz kısımlar kırpılarak sadece yüz bölgesine odaklanılarak başka bir çalışma daha gerçekleştirilmiştir. Bu uygulamalar için çekişmeli üretici ağlar (GAN) tabanlı bir derin öğrenme modeli geliştirilmiştir. Sonuçların başarı performansı görüntü kalite metrikleri PSNR (tepe sinyal gürültü oranı) ve SSIM (yapısal benzerlik endeksi) ile değerlendirmeye alınmıştır. Sadece yüz bölgelerine odaklanılarak gerçekleştirilen uygulamanın sonuçları orijinal görüntülerle yapılan uygulama sonuçlarına kıyasla daha iyi olduğu görülmüştür. Bunun yanı sıra bu çalışma, daha az maliyetli termal kamera tarafından elde edilen termal görüntülerin çözünürlüğünü, yüksek maliyete sahip olan ve yüksek kalitede görüntüler elde edilebilen termal kameranın çözünürlüğüne bilhassa görsel olarak yaklaştırma yönünden olumlu sonuçlar vermiştir.
1 citations
DOI•
TL;DR: In this article , a fully automated workflow based on soft computing to characterize the heterogeneous flow properties of cores for predictive continuum-scale models is proposed, where image features and morphological properties provide sufficient measures for petrophysical classification.
Abstract: The influence of core‐scale heterogeneity on continuum‐scale flow and laboratory measurements are not well understood. To address this issue, we propose a fully automated workflow based on soft computing to characterize the heterogeneous flow properties of cores for predictive continuum‐scale models. While the proposed AI‐based workflow inherently has no trained knowledge of rock petrophysical properties, our results demonstrate that image features and morphological properties provide sufficient measures for petrophysical classification. Micro X‐ray computed tomography (μxCT) image features were extracted from full core plug images by using a Convolutional Neural Network and Minkowski functional measurements. The features were then classified into specific classes using Principal Component Analysis followed by K‐means clustering. Next, the petrophysical properties of each class were evaluated using pore‐scale simulations to substantiate that unique classes were identified. The μxCT image was then up‐scaled to a continuum‐scale grid based on the defined classes. Last, simulation results were evaluated against real‐time flooding data monitored by Positron Emission Tomography. Both homogeneous sandstone and heterogeneous carbonate were tested. Simulation and experimental saturation profiles compared well, demonstrating that the workflow provided high‐fidelity characterization. Overall, we provided a novel workflow to build digital rock models in a fully automated way to better understand the impacts of heterogeneity on flow.
1 citations
References
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TL;DR: In this article, a structural similarity index is proposed for image quality assessment based on the degradation of structural information, which can be applied to both subjective ratings and objective methods on a database of images compressed with JPEG and JPEG2000.
Abstract: Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a structural similarity index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000. A MATLAB implementation of the proposed algorithm is available online at http://www.cns.nyu.edu//spl sim/lcv/ssim/.
40,609 citations
Book•
23 May 2011TL;DR: It is argued that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas.
Abstract: Many problems of recent interest in statistics and machine learning can be posed in the framework of convex optimization. Due to the explosion in size and complexity of modern datasets, it is increasingly important to be able to solve problems with a very large number of features or training examples. As a result, both the decentralized collection or storage of these datasets as well as accompanying distributed solution methods are either necessary or at least highly desirable. In this review, we argue that the alternating direction method of multipliers is well suited to distributed convex optimization, and in particular to large-scale problems arising in statistics, machine learning, and related areas. The method was developed in the 1970s, with roots in the 1950s, and is equivalent or closely related to many other algorithms, such as dual decomposition, the method of multipliers, Douglas–Rachford splitting, Spingarn's method of partial inverses, Dykstra's alternating projections, Bregman iterative algorithms for l1 problems, proximal methods, and others. After briefly surveying the theory and history of the algorithm, we discuss applications to a wide variety of statistical and machine learning problems of recent interest, including the lasso, sparse logistic regression, basis pursuit, covariance selection, support vector machines, and many others. We also discuss general distributed optimization, extensions to the nonconvex setting, and efficient implementation, including some details on distributed MPI and Hadoop MapReduce implementations.
17,433 citations
TL;DR: It is shown that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation, and an algorithm called LARS‐EN is proposed for computing elastic net regularization paths efficiently, much like algorithm LARS does for the lamba.
Abstract: Summary. We propose the elastic net, a new regularization and variable selection method. Real world data and a simulation study show that the elastic net often outperforms the lasso, while enjoying a similar sparsity of representation. In addition, the elastic net encourages a grouping effect, where strongly correlated predictors tend to be in or out of the model together.The elastic net is particularly useful when the number of predictors (p) is much bigger than the number of observations (n). By contrast, the lasso is not a very satisfactory variable selection method in the
16,538 citations
"Image super-resolution" refers background in this paper
...As the l2 norm represents a smoothing prior and the l1 norm tends to preserve the edges, the lp ( ≤ ≤ p 1 2) norm achieves a balance between them, thereby avoiding the staircase effect [110]....
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TL;DR: In this article, a constrained optimization type of numerical algorithm for removing noise from images is presented, where the total variation of the image is minimized subject to constraints involving the statistics of the noise.
15,225 citations
"Image super-resolution" refers background in this paper
...[93,103], based on the fact that an image is naturally “blocky” and discontinuous....
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Book•
01 Jan 1977
8,009 citations
"Image super-resolution" refers background in this paper
...In the early years, the smoothness of natural images was mainly considered, which leads to the quadratic property of the regularizations [99,100]....
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