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
More filters
TL;DR: This study aims to present a novel approach of verifying an individual’s signature verification using biometrics and document forensics for offline mode verification.
Abstract: One of the major problems in biometrics and in document forensics is the offline mode of signature verification. This study aims to present a novel approach of verifying an individual’s signature t...
9 citations
TL;DR: A unified framework based on rank minimization (UFRM) is proposed for use with multiangle multi/hyperspectral remote sensing images, which simultaneously integrates image super-resolution reconstruction (SRR) and image registration.
Abstract: In this article, a unified framework based on rank minimization (UFRM) is proposed for use with multiangle multi/hyperspectral remote sensing images, which simultaneously integrates image super-resolution reconstruction (SRR) and image registration. With the complementary information of different angle images and the high correlation between each band of the multi/hyperspectral images, a new image observation model is established to describe the mathematical degradation process of the observed low-resolution multiangle multi/hyperspectral images from the desired high-resolution (HR) multi/hyperspectral image. Based on the rank-one structure of the multiangle images, each observed image is decomposed into a foreground image for each angle image, and a background image, which is shared among all the multiangle images. A multichannel total variation constraint is applied to the target HR background image, with the consideration of the high correlation of different bands. Finally, an alternating minimization optimization strategy is utilized to resolve the joint cost function, which consists of the unknown image registration transformation parameters and the desired reconstruction image. As a result, the UFRM method can simultaneously achieve image registration and SRR. A number of experiments were conducted, which confirmed the superior performance of the proposed method.
9 citations
Cites background from "Image super-resolution"
...In order to improve the spatial resolution of the observed images, one effective solution is to develop more advanced optical imaging devices, which is referred to as the “hardware approach” [11]....
[...]
TL;DR: The use of super resolution by deep learning to improve the manual and automatic detection of boulders in backscatter mosaics is explored and it is found that upscaling of mosaics by a factor of 2 increases the performance of small boulder detection and boulder density grids.
Abstract: In marine habitat mapping, a demand exists for high-resolution maps of the seafloor both for marine spatial planning and research. One topic of interest is the detection of boulders in side scan sonar backscatter mosaics of continental shelf seas. Boulders are oftentimes numerous, but encompass few pixels in backscatter mosaics. Therefore, both their automatic and manual detection is difficult. In this study, located in the German Baltic Sea, the use of super resolution by deep learning to improve the manual and automatic detection of boulders in backscatter mosaics is explored. It is found that upscaling of mosaics by a factor of 2 to 0.25 m or 0.125 m resolution increases the performance of small boulder detection and boulder density grids. Upscaling mosaics with 1.0 m pixel resolution by a factor of 4 improved performance, but the results are not sufficient for practical application. It is suggested that mosaics of 0.5 m resolution can be used to create boulder density grids in the Baltic Sea in line with current standards following upscaling.
9 citations
TL;DR: A robust spatially-transformed deep learning framework is established to simultaneously perform both the geometric transformation and the single image super-resolution, which achieves a high level of robustness against a number of geometric transformations, including scaling, translations and rotations.
Abstract: In general, existing research on single image super-resolution does not consider the practical application that, when image transmission is over noisy channels, the effect of any possible geometric transformations could incur significant quality loss and distortions. To address this problem, we present a new and robust super-resolution method in this paper, where a robust spatially-transformed deep learning framework is established to simultaneously perform both the geometric transformation and the single image super-resolution. The proposed seamlessly integrates deep residual learning based spatial transform module with a very deep super-resolution module to achieve a robust and improved single image super-resolution. In comparison with the existing state of the arts, our proposed robust single image super-resolution has a number of novel features, including 1) content-characterized deep features are extracted out of the input LR images to identify the incurred geometric transformations, and hence transformation parameters can be optimized to influence and control the super-resolution process; 2) the effects of any geometric transformations can be automatically corrected at the output without compromise on the quality of final super-resolved images; and 3) compared with the existing research reported in the literature, our proposed achieves the advantage that HR images can be recovered from those down-sampled LR images corrupted by a number of different geometric transformations. The extensive experiments, measured by both the peak-signal-to-noise-ratio and the similar structure index measurement, show that our proposed method achieves a high level of robustness against a number of geometric transformations, including scaling, translations, and rotations. Benchmarked by the existing state-of-the-arts SR methods, our proposed delivers superior performances on a wide range of datasets which are publicly available and widely adopted across relevant research communities.
9 citations
Cites methods from "Image super-resolution"
...While the advantage of these methods is the computational efficiency, these methods have some limitations in incorporate the image prior knowledge [17]....
[...]
TL;DR: In this article, a recursive Deep Image Prior (DIP) based method was proposed to capture cell dynamics and interactions from recorded experiments by TLM. But, due to physical and cost limitations, acquiring high resolution videos is not always possible.
8 citations
References
More filters
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]....
[...]
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....
[...]
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]....
[...]