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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11 Jul 2021
TL;DR: Wang et al. as mentioned in this paper proposed a recurrent refinement network to refine the results of single-image single-view SR (SISR) method for satellite video recovery, where the complementary information enhanced by an encoder-decoder structure from adjacent frames was learned to refine SISR results.
Abstract: Deep learning-based methods have shown superior performance in VSR tasks. However, satellite video frames are characterized by large width, low resolution, and lack of features. Consequently, the conventional VSR method is not suitable for satellite video. In this paper, a recurrent refinement network is proposed. Considering that the vast majority of remote sensing images belong to the static background, a single-image SR (SISR) method is first used to obtain high-resolution features for a specific target frame. To further complement the missing details, the network learns the complementary information enhanced by an Encoder-Decoder structure from adjacent frames to refine the results of SISR. To measure the contribution of different adjacent frames to the recovery of the target frame, a temporal attention mechanism is introduced in the final fusion stage. The experiment on the video data of Jilin-1 demonstrates the effectiveness of our method.
2 citations
TL;DR: Compared with state-of-the-art methods, the proposed GAN-IMC method not only achieves competitive perceptual index and natural image quality evaluator values but also obtains pleasant visual perception in edge, texture, color, and feature-rich regions.
Abstract: Generative adversarial network (GAN) for image super-resolution (SR) has attracted enormous interests in recent years. However, the GAN-based SR methods only use image discriminator to distinguish SR images and high-resolution (HR) images. Image discriminator fails to discriminate images accurately since image features cannot be fully expressed. We design a GAN-based SR framework GAN-IMC, which includes generator, image discriminator, morphological component discriminator, and color discriminator. The combination of multiple feature discriminators improves the accuracy of image discrimination. Adversarial training between the generator and multifeature discriminators forces SR images to converge with HR images in terms of data and features distribution. Moreover, in some cases, feature enhancement of feature-rich region is also worth considering. GAN-IMC is further optimized by weighted content loss (GAN-IMCW), which effectively restores and enhances feature-rich regions in SR images. The effectiveness and robustness of the proposed method are confirmed by extensive experiments on public datasets. Compared with state-of-the-art methods, the proposed method not only achieves competitive perceptual index and natural image quality evaluator values but also obtains pleasant visual perception in edge, texture, color, and feature-rich regions.
2 citations
TL;DR: Zhang et al. as discussed by the authors proposed a generative image steganalysis algorithm based on focused feedback residual convolutional neural network for simultaneous detection and extraction of hidden information, which can obtain state-of-the-art results in terms of detection rate and hidden information reconstruction compared with classical rich models and several deep learning-based methods.
2 citations
06 Oct 2020
TL;DR: In this paper, the authors proposed a super-resolution wide remote sensing residual network (WRSR) which increases the width and reduces the depth of the residual network, which reduced memory costs.
Abstract: To improve the resolution of satellite images, many researchers are committed to machine learning and neural network-based SR methods. SR has multiple residual network frameworks in deep learning that have improved performance and can extend thousands of layers in the system. However, each layer improves accuracy by doubling the number of layers, although training thousands of layers are too expensive, the process is slow, and there are functional recovery issues. To address these issues, we propose a super-resolution wide remote sensing residual network (WRSR), in which we increase the width and reduce the depth of the residual network, due to decreasing the depth of the network our model reduced memory costs. To enhance the resolution of the single image we showed that our method improves training loss performance by performing the weight normalization instead of augmentation technology. The results of the experiment show that the method performs well in terms of quantitative indicators (PSNR) and (SSIM).
2 citations
TL;DR: An optical device including a camera and a wedge waveguide that is optimized for imaging within confined spaces in archeology, to redirect light by total internal reflection to circumvent the lack of room, and to compute the final image from the raw data.
Abstract: Acquiring images of archaeological artifacts is an essential step for the study and preservation of cultural heritage. In constrained environments, traditional acquisition techniques may fail or be too invasive. We present an optical device including a camera and a wedge waveguide that is optimized for imaging within confined spaces in archeology. The major idea is to redirect light by total internal reflection to circumvent the lack of room, and to compute the final image from the raw data. We tested various applications on site during an archaeological mission in Medamoud (Egypt). Our device was able to successfully record images of the underground from slim trenches of about 15cm wide, including underwater trenches, and between rocks composing a wall temple. Experts agreed that the acquired images were good enough to get useful information that cannot be obtained as easily with traditional techniques.
2 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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