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.
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01 May 2018
TL;DR: In this article, the adaptive group-based sparse domain selection (A-GSDS) method is proposed to enforce the local smoothness and non-local self-similarity by sparse representation in a unified framework.
Abstract: sparse representation has been used as a powerful statistical image modelling technique in single image super resolution (SISR). Although this prior efficiently is utilized to describe the local smoothness but ignoring the correlation between the sparse representation coefficients of similar patches can lead inaccurate spare coding coefficients. In this paper, we propose the method that enforce the local smoothness and nonlocal self-similarity by sparse representation in a unified framework, called adaptive group-based sparse domain selection (A-GSDS). N onlocal patches with similar structures are leveraged and stacked into a matrix as the basic unit of sparse representation called group. These groups are converted into a column vector, each column selects the best fitted PCAA sub dictionary learned from the training data. After applying the sparse coding process to each column in the group domain, sparse vectors are obtained by orthogonal sub dictionaries which can be easily estimated. To further improve the performance of the group-based sparse representation, we use nonlocal means regularization term. Extensive experimental results validate the effectiveness of the proposed method comparing with the state-of-the-art algorithms.
1 citations
TL;DR: In this article , the authors focus on detecting artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once).
Abstract: In the last two decades, unmanned aerial vehicle (UAV) technology has been widely utilized as an aerial survey method. Recently, a unique system of self-deployable and biodegradable microrobots akin to winged achene seeds was introduced to monitor environmental parameters in the air above the soil interface, which requires geo-localization. This research focuses on detecting these artificial seed-like objects from UAV RGB images in real-time scenarios, employing the object detection algorithm YOLO (You Only Look Once). Three environmental parameters, namely, daylight condition, background type, and flying altitude, were investigated to encompass varying data acquisition situations and their influence on detection accuracy. Artificial seeds were detected using four variants of the YOLO version 5 (YOLOv5) algorithm, which were compared in terms of accuracy and speed. The most accurate model variant was used in combination with slice-aided hyper inference (SAHI) on full resolution images to evaluate the model’s performance. It was found that the YOLOv5n variant had the highest accuracy and fastest inference speed. After model training, the best conditions for detecting artificial seed-like objects were found at a flight altitude of 4 m, on an overcast day, and against a concrete background, obtaining accuracies of 0.91, 0.90, and 0.99, respectively. YOLOv5n outperformed the other models by achieving a mAP0.5 score of 84.6% on the validation set and 83.2% on the test set. This study can be used as a baseline for detecting seed-like objects under the tested conditions in future studies.
1 citations
TL;DR: In this article, a bed-boundar NMR interpretation method was proposed to estimate in situ rock and fluid properties. But the interpretation method often neglects bedboundar properties.
Abstract: Borehole measurements of nuclear magnetic resonance (NMR) are routinely used to estimate in situ rock and fluid properties. Conventional NMR interpretation methods often neglect bed-boundar...
1 citations
01 Aug 2021
TL;DR: HR images generated by applying super resolution to low resolution face images improve the image quality in terms of Mean squared error (MSE), Structural similarity index measure (SSIM) and Peak to signal noise ratio (PSNR), however, the results indicate that improvement in the imagequality does not significantly improve performance of deep model.
Abstract: Face images captured in unconstrained environment differ in various aspects such as expression, illumination, resolution, occlusion, pose etc. which makes face recognition task difficult. The face images captured by the camera from a distance will have low resolution and lack many finer details that makes face recognition a challenging task. Super resolution (SR) is a process of generating high resolution (HR) images from one or more images. In this work, we apply super resolution to low resolution (LR) images of faces to find the impact on the deep models performance. To achieve this, we create dataset with face images captured in unconstrained environment. Later we designed a CNN model with eight layers and trained on the dataset created. Our deep model with low memory requirement and less parameters achieves an accuracy of 99.75% on test dataset and outperforms fine-tuned VGGFace by a small margin. The performance of our deep neural network and fine-tuned VGGFace was observed on low resolution images pre and post-super resolution. The deep neural network-based model available in OpenCV, SRGAN super resolution model and INTER_CUBIC interpolation are used to generate HR images. The HR images generated by OpenCV, SRGAN are better than INTER_CUBIC interpolation. The results show that HR images generated by applying SR to low resolution face images improve the image quality in terms of Mean squared error (MSE), Structural similarity index measure (SSIM) and Peak to signal noise ratio (PSNR). However, the results indicate that improvement in the image quality does not significantly improve performance of deep model.
1 citations
Posted Content•
TL;DR: This work evaluates the proposed end-to-end convolutional neural network approach on two different optical flow estimation mehods and shows that it can not only obtain the full image resolution, but generate more accurate optical flow field with sharper edges than the estimation result of original method.
Abstract: The convolutional neural network model for optical flow estimation usually outputs a low-resolution(LR) optical flow field. To obtain the corresponding full image resolution,interpolation and variational approach are the most common options, which do not effectively improve the results. With the motivation of various convolutional neural network(CNN) structures succeeded in single image super-resolution(SISR) task, an end-to-end convolutional neural network is proposed to reconstruct the high resolution(HR) optical flow field from initial LR optical flow with the guidence of the first frame used in optical flow estimation. Our optical flow super-resolution(OFSR) problem differs from the general SISR problem in two main aspects. Firstly, the optical flow includes less texture information than image so that the SISR CNN structures can't be directly used in our OFSR problem. Secondly, the initial LR optical flow data contains estimation error, while the LR image data for SISR is generally a bicubic downsampled, blurred, and noisy version of HR ground truth. We evaluate the proposed approach on two different optical flow estimation mehods and show that it can not only obtain the full image resolution, but generate more accurate optical flow field (Accuracy improve 15% on FlyingChairs, 13% on MPI Sintel) with sharper edges than the estimation result of original method.
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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