- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
[IEEE 2019 41st Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC) - Berlin, Germany (2019.7.23-2019.7.27)] 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) - Super-Resolution OCT Using Sparse Representations and Heavy-Tailed Models
摘要： This paper introduces a new approach to single-image super-resolution in Optical Coherence Tomography (OCT) images. Retinal OCT images can be used to diagnose various diseases, not only peculiar to the eye, but also some systemic diseases. Nevertheless, as with any imaging modality, the acquired images suffer from degradation due to various causes. To overcome this and enhance image quality, Super-Resolution (SR) techniques are widely used. This work explores a convex regularization approach based on a multivariate generalization of the minimax-concave (GMC) scheme in a forward-backward splitting (FBS) scheme. Based on the assumption that sparse representations of OCT images are heavy-tailed, an α-stable dictionary is employed. This approach is implemented with overlapping and non-overlapping patches. Since the Point Spread Function (PSF) of the images used is generally unknown, it is estimated using a method originally proposed for ultrasound images. The algorithm is tested on OCT images of murine eyes. The results show that the proposed convex regularization method provides results that are competitive with the state-of-the-art. Indeed, significant deblurring and quality enhancement are achieved using the proposed algorithm and in most cases it provides the best results, both objectively and subjectively.
关键词： Super-Resolution,Sparse Representations,Optical Coherence Tomography,Heavy-Tailed Models,Convex Regularization
更新于2025-09-11 14:15:04
-
Spectral super-resolution spectroscopy using a random laser
摘要： Super-resolution microscopy refers to a powerful set of imaging techniques that overcome the diffraction limit. Some of these techniques, the importance of which was recognized by the 2014 Nobel Prize for chemistry, are based on the concept of image reconstruction by spatially sparse sampling. Here, we introduce the concept of super-resolution spectroscopy based on sparse sampling in the frequency domain, and show that this can be naturally achieved using a random laser source. In its chaotic regime, the emission spectrum of a random laser features sharp spikes at uncorrelated frequencies that are sparsely distributed over the emission bandwidth. These narrow lasing modes probe stochastically the spectral response of a sample, allowing it to be reconstructed with a resolution exceeding that of the spectrometer. We envision that the proposed technique will inspire a new generation of simple, cheap, high-resolution spectroscopy tools with a reduced footprint.
关键词： super-resolution spectroscopy,random laser,sparse sampling,spectral reconstruction,frequency domain
更新于2025-09-11 14:15:04
-
[IEEE 2019 21st International Conference on Transparent Optical Networks (ICTON) - Angers, France (2019.7.9-2019.7.13)] 2019 21st International Conference on Transparent Optical Networks (ICTON) - Direct Laser Writing Using Chalcogenide Thin Films
摘要： Direct laser writing has been performed in thin AMTIR-1 layers. By using 10 ns laser pulses, 250 nm thick lines have been written by tightly focusing the laser beam on the thin film layers. The possibility to enhance this resolution by using the Sb2Te3 material as super-resolution mask is also discussed.
关键词： saturable absorption,direct laser writing,Z-scan,super-resolution,nonlinear optics,chalcogenide glasses
更新于2025-09-11 14:15:04
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Single Frame Super Resolution with Convolutional Neural Network for Remote Sensing Imagery
摘要： In this paper, a new convolutional neural networks based super-resolution (SR) is proposed. SR has been a hot research area for decades, and it includes two types: single frame based SR and multi-frame based SR. The focus of the paper is to reconstruct the corresponding high resolution image from a given low resolution image. The popular end-to-end learning architecture is improved and no preprocessing and image aggregation are needed. Our network model (RSCNN) uses different convolution kernels for a set of feature maps in the feature mapping step, which ensures the accuracy of reconstruction results under the premise of improving the reconstruction quality. The method is applied to Jilin-1 which is the first self-developed commercial remote sensing satellite group in China. The results show the superiority of our method both visually and numerically by comparing with other excellent image super resolution algorithms.
关键词： Jinlin-1 satellite,Single image super resolution,Convolutional Neural Network
更新于2025-09-11 14:15:04
-
Stereoscopic Image Super-Resolution Method with View Incorporation and Convolutional Neural Networks
摘要： Super-resolution (SR) plays an important role in the processing and display of mixed-resolution (MR) stereoscopic images. Therefore, a stereoscopic image SR method based on view incorporation and convolutional neural networks (CNN) is proposed. For a given MR stereoscopic image, the left view of which is observed in full resolution, while the right view is viewed in low resolution, the SR method is implemented in two stages. In the first stage, a view difference image is defined to represent the correlation between views. It is estimated by using the full-resolution left view and the interpolated right view as input to the modified CNN. Accordingly, a high-precision view difference image is obtained. In the second stage, to incorporate the estimated right view in the first stage, a global reconstruction constraint is presented to make the estimated right view consistent with the low-resolution right view in terms of the MR stereoscopic image observation model. Experimental results demonstrated that, compared with the SR convolutional neural network (SRCNN) method and depth map based SR method, the proposed method improved the reconstructed right view quality by 0.54 dB and 1.14 dB, respectively, in the Peak Signal to Noise Ratio (PSNR), and subjective evaluation also implied that the proposed method produced better reconstructed stereoscopic images.
关键词： view difference,mixed-resolution stereoscopic image,super-resolution,convolutional neural networks,stereoscopic imaging and coding
更新于2025-09-10 09:29:36
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Deep Image- To-Image Transfer Applied to Resolution Enhancement of Sentinel-2 Images
摘要： Single Image Super-Resolution (SISR) is looking at restoring the missing high-resolution information from a single low-resolution image in order to increase the apparent spatial resolution by a factor of two or more. In recent years, convolution neural networks have been applied with great success to the problem of improving spatial resolution from a single image. With the advent of low-resolution (10m) optical sensors such as Sentinel-2, it is interesting to explore the possibility of improving image resolution with Deep Learning (DL) techniques. The purpose of this article is to investigate the potential performances of recent DL super-resolution techniques. The techniques explored here include not only techniques for enhancing high-frequency content but also so-called image-to-image translation techniques based on Generative Adversarial Neural Networks (GAN). From our preliminary results, we show that GANs have the ability to restore complex textural information.
关键词： GAN,Deep Learning,Optical Images,Super-Resolution
更新于2025-09-10 09:29:36
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Sub-Pixel Mapping with Hyperspectral Images Using Super-Resolution
摘要： Hyperspectral images are rich in spectral content but their spatial resolution is relatively poor. It can lead to mixed pixels and sub-pixel targets. In order to improve the reliability of information provided by hyperspectral image analysis and make the results practically usable, one needs to improve their spatial resolution. Due to physical constraints and associated cost, increasing the resolution by improving the sensors may not be a practical option. Thus one effective solution is some form of post-processing of hyperspectral data. Such an algorithmic resolution enhancement is called “super-resolution”. In this paper single image super-resolution of hyperspectral image has been attempted. The use of Hopfield Neural Network for successful landuse/landcover classification of Hyperspectral image has been shown. A successful attempt was made to improve initialization of the Hopfield neural network. The results were verified visually as well as statistically.
关键词： Sub-pixel Mapping,Hyperspectral Image,Landuse/landcover,Mixed Pixel,Super-resolution,Hopfield Neural Network
更新于2025-09-10 09:29:36
-
[IEEE 2018 24th International Conference on Pattern Recognition (ICPR) - Beijing, China (2018.8.20-2018.8.24)] 2018 24th International Conference on Pattern Recognition (ICPR) - Enhancing OCR Accuracy with Super Resolution
摘要： Accuracy of OCR is often marred by the poor quality of the input document images. Generally this performance degradation is attributed to the resolution and quality of scanning. This calls for special efforts to improve the quality of document images before passing it to the OCR engine. One compelling option is to super-resolve these low resolution document images before passing them to the OCR engine. In this work we address this problem by super-resolving document images using Generative Adversarial Network (GAN). We propose a super resolution based preprocessing step that can enhance the accuracies of the OCRs (including the commercial ones). Our method is specially suited for printed document images. We validate the utility in wide variety of document images (where fonts, styles, and languages vary) without any pre-processing step to adapt across situations. Our experiments show an improvement upto 21% in accuracy OCR on test images scanned at low resolution. One immediate application of this can be in enhancing the recognition of historic documents which have been scanned at low resolutions.
关键词： OCR,Super-Resolution,Document Images,GAN
更新于2025-09-10 09:29:36
-
[IEEE IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Valencia, Spain (2018.7.22-2018.7.27)] IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium - Robust Super-Resolution Image Reconstruction Method for Geometrically Deformed Remote Sensing Images
摘要： Due to the limitations of imaging sensors, remote sensing images often have limited resolution. To address this issue, various super-resolution (SR) image reconstruction techniques have been developed to reconstruct a high-resolution image from a sequence of low-resolution, noisy and blurry observations. In this paper, we propose an efficient super-resolution image reconstruction method for geometrically deformed remote sensing images, based on the nonlocal total variation (NLTV) regularization. The proposed minimization problem is solved by a fast primal-dual algorithm. Numerical experiments demonstrate the performance of the proposed method.
关键词： super-resolution image reconstruction,Remote sensing images,primal-dual algorithm
更新于2025-09-10 09:29:36
-
[Lecture Notes in Computer Science] Pattern Recognition and Computer Vision Volume 11257 (First Chinese Conference, PRCV 2018, Guangzhou, China, November 23-26, 2018, Proceedings, Part II) || Image Super-Resolution Based on Dense Convolutional Network
摘要： Recently, the performance of single image super-resolution (SISR) methods have been significantly improved with the development of the convolutional neural networks (CNN). In this paper, we propose a very deep dense convolutional network (SRDCN) for image super-resolution. Due to the dense connection, the feature maps of each preceding layer are connected and used as inputs of all subsequent layers, thus utilizing both low-level and high-level features. In addition, residual learning and dense skip connection are adopted to ease the difficulties of training very deep convolutional networks by alleviating the vanishing-gradient problem. Experimental results on four benchmark datasets demonstrate that our proposed method achieves comparable performance with other state-of-the-art methods.
关键词： Single image super-resolution,Residual learning,Dense convolutional network
更新于2025-09-10 09:29:36