Example-based super-resolution for single-image analysis from the Chang'e-1 Mission

Example-based super-resolution (SR) methods learn the correspondences between low resolution (LR) and high-resolution (HR) image patches, where the patches are extracted from a training database. To reconstruct a single LR image into a HR one, each LR image patch is processed by the previously trained model to recover its corresponding HR patch. For this reason, they are computationally inefficient. We propose the use of a selective patch processing technique to carry out the super-resolution step more efficiently, while maintaining the output quality. In this technique, only patches of high variance are processed by the costly reconstruction steps, while the rest of the patches are processed by fast bicubic interpolation. We have applied the proposed improvement on representative example-based SR methods to super-resolve text images. The results show a significant speed up for text SR without a drop in theocrat accuracy. In order to carry out an extensive and solid performance evaluation, we also present a public database of text images for training and testing example-based SR methods.

Zoom based image super-resolution using DCT with LBP as characteristic model

Studies have indicated that the decrease in the extent of Arctic sea ice in recent years has had a significant impact on the Arctic ecosystem and global climate. In order to understand the evolution of sea ice, it is becoming increasingly imperative to have continuous observations of Arctic-wide sea ice with high spatial resolution. Passive microwave sensors have the benefit of being less susceptible to weather, wider coverage, and higher temporal resolution. However, it is challenging to retrieve accurate parameters of sea ice due to the low spatial resolution of passive microwave images. Therefore, improving the spatial resolution of passive microwave images is beneficial for reducing the uncertainty of sea ice parameters. In this paper, four competitive multi-image super-resolution (MISR) networks are selected to explore the applicability of the networks on multi-frequency Advanced Microwave Scanning Radiometer 2 (AMSR2) images of Arctic sea ice. The upsampling factor is set to 4 in the experiment. Firstly, the optimal input lengths of the image sequence for the four MISR networks are found, and then the best network on different frequency band images is further identified. Furthermore, some factors, including seasons, sea ice motion, and polarization mode of images, that may affect the super-resolution (SR) results are analyzed. The experimental results indicate that utilizing images from winter yields superior SR results. Conversely, SR results are the worst during summer across all four MISR networks, exhibiting the largest difference in PSNR of 4.48 dB. Additionally, the SR performance is observed to be better for images with smaller magnitudes of sea ice motion compared to those with larger motions, with the maximum PSNR difference of 2.04 dB. Finally, the SR results for vertically polarized images surpass those for horizontally polarized images, showcasing an average advantage of 4.02 dB in PSNR and 0.0061 in SSIM. In summary, valuable suggestions for selecting MISR models for passive microwave images of Arctic sea ice at different frequency bands are offered in this paper. Additionally, the quantification of the various impact factors on SR performance is also discussed in this paper, which provides insights into optimizing MISR algorithms for passive microwave sea ice imagery.

Deep Feature Blend Attention: A New Frontier in Super Resolution Image Generation

Super-resolution (SR) reconstruction is a technique to yield a higher resolution (HR) image from aliasing low resolution (LR) ones. An LR image is upsampled as the initialization, and then iteratively corrected in comparison with the other LR images. As the solution satisfying the SR constraints is non-unique, it is impossible to recover the original HR details completely by SR techniques. The solution reconstructed is sensitive to the starting point, especially when LR observations are insufficient, and may converge to a local optimum point. SR images reconstructed with different initializations may diverge in different ways from the true HR image. The influence of the initial HR estimate has not been sufficiently addressed so far by existing SR methods. We will explore this initial image selection issue to improve the performance of SR reconstruction.

Multi-modal imaging requires image fusion to combine advantages of different types of sensors and requires super-resolution (SR) because of limited spatial resolution of source images. In this study, a novel framework is proposed for unification of image SR and the fusion process to obtain a high-resolution (HR)-fused image from a set of low-resolution (LR) multi-modal images. The jointly trained dictionaries of LR patches and corresponding HR patches are used for sparse representation of LR source image patches and HR-fused image patches, respectively. The sparse coefficients vectors for corresponding patches of source LR images are determined by using orthogonal matching pursuit and a local information content-based metric is employed to fuse these sparse coefficients. The corresponding HR-fused image patch is obtained by combining elements of the HR dictionary as per the fused coefficients of the LR image patches. The experimental results on sets of multi-modal images exhibited that the proposed method outperformed the existing fusion and SR techniques in terms of visual quality and image fusion quality metrics.

Superresolution reconstruction (SRR) is an effective approach for improving spatial resolution of image, which need not change the original imaging system hardware. Among the SRR methods, jointly estimate image registration and superresolution can overcome the serve aliased between low resolution images. However, further studies are still necessary for choose appropriate prior mode for better preserving edge of image. Also it is important for the choice of adaptive regularization parameters in the SRR based on regularization/maximum a posteriori (MAP) method. In this paper, we propose a method for joint image registration and SRR method based on MAP framework. In the reconstruction, the initial high resolution image is firstly obtained by the shift-and-add method. Then, the edge-preserved MRF model is used to improve the reconstructed image quality. Moreover, adaptive regularization method is used in the SRR. Experimental results demonstrate our improved SRR method effective.

Polar sea ice is one of the key parameters of cryosphere and polar environmental change, which plays an important role in the study of global climate change. High-resolution monitoring of polar sea ice relies mainly on optical satellite imagery and synthetic aperture radar (SAR) data, with limited spatial and temporal coverages for many applications. Passive microwave data is an important data source for continuous observations of polar sea ice, thanks to its working ability in all-sky conditions and its wide coverage. However, it is difficult to achieve high-resolution monitoring of polar sea ice using passive microwave data due to its coarse resolution. In order to solve this problem, super resolution (SR) reconstruction technique is adopted in this paper to improve the spatial resolution of passive microwave images. SR reconstruction technique based on both single-image and multi-image are attempted. AMSR2 level 3 (L3) Products of Brightness Temperatures (BTs) for Arctic sea ice are used as experimental data, and the reconstruction results obtained from different SR methods are compared and discussed.

DHBSR: A deep hybrid representation-based network for blind image super resolution

Spatial resolution of image is a critical index of image quality, and also is an important parameter in the area of image application. However, there are many factors which can lower and degenerate the quality of images in the process of capturing images. An effective solution to this problem is the super-resolution image reconstruction, that means reconstructing a high resolution (HR) image from some low resolution (LR) images, and eliminating the additive noise and blur which is caused by the size of the detector and optical at the same time. In this paper,one of project of convex set method for reconstructing a super-resolution image from multiframe undersampling images is introduced.First,a very important factor-the model of the process of imaging in spatial domain iterative method for super-resolution image reconstruction is mentioned. Next the reconstruction model and its steps are introduced through introducing the theory of project of convex set method. At last, the technique is available by experiments. The result indicates the project of convex set method for super-resolution image reconstruction has preferable effect on multiframe undersampling remote sensing images, and the technique of super-resolution image reconstruction can used in lots of fields such as remote sensing, computer vision, public security, physic imaging and so on.

Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network

In view of the limitation in term of direction that the existing interpolation reconstruction algorithm of single image and super resolution (SR) image cannot fully utilize the specific geometric features of the image itself, this paper proposed a novel SR algorithm of single image based on the integration of Bandelet geometric-flow-oriented interpolation and total variation in wavelet domain (IBITV). This method seamlessly took advantages of both wavelet and super-wavelet transformations. Firstly, the framework of image SR reconstruction and optimization was presented based on Bandelet and wavelet transformation. The framework made full use of the acquisition ability of the accurately geometric flow characteristics of the image based on Bandelet transformation. Furthermore, it introduced the idea that Bandelet transformed two-dimensional image and one-dimensional signal were projection mapping interactively, and the structures of image are similar at the same high frequency components of different scales after wavelet transformation to apply the Bandelet precise direction discrimination ability rather than sparse representation ability. Finally, in order to avoid the possibility of block effect, the influence of difference between low frequency component of the high resolution image and low resolution image on the final reconstructed image was considered based on the initially estimated image with high resolution. The total variation method of wavelet domain was utilized to carry out iterative solution of SR so as to obtain the final high resolution image. Experimental results show that the proposed method achieved favorable results in both iterative convergence characteristic of the algorithm and the image SR.

Medical image super-resolution reconstruction algorithms based on deep learning: A survey

The size of pixels in a digital recording device, such as a CCD array, limits the spatial resolution in images obtained by an optical imaging system, thereby degrading the image quality. Digital super-resolution (DSR) techniques are used to reconstruct a high-resolution (HR) image from multiple sub-pixel-shifted low-resolution images in order to improve the image quality. In this article, we formulate a mathematical framework for DSR using the generalized sampling theorem (GST). The GST-based DSR method’s performance is evaluated by comparing it to existing resolution enhancement methods on the basis of evaluation metrics like percentage mean square error (%MSE), structural similarity index measure (SSIM), and peak signal-to-noise ratio (PSNR). The GST DSR method exhibits an overall superior quality image reconstruction based on quantitative analysis (with a near zero %MSE, SSIM around one, and improved PSNR in dB) and qualitative comparison. The robustness of the GST DSR method is further demonstrated in the presence of frame-to-frame shift estimation error using %MSE and SSIM by comparing it with multi-frame interpolation approaches.

Learning based single image super-resolution (SISR) for real-world images has been an active research topic yet a challenging task, due to the lack of paired low-resolution (LR) and high-resolution (HR) training images. Most of the existing unsupervised real-world SISR methods adopt a two-stage training strategy by synthesizing realistic LR images from their HR counterparts first, then training the super-resolution (SR) models in a supervised manner. However, the training of image degradation and SR models in this strategy are separate, ignoring the inherent mutual dependency between downscaling and its inverse upscaling process. Additionally, the ill-posed nature of image degradation is not fully considered. In this paper, we propose an image downscaling and SR model dubbed as SDFlow, which simultaneously learns a bidirectional many-to-many mapping between real-world LR and HR images unsupervisedly. The main idea of SDFlow is to decouple image content and degradation information in the latent space, where content information distribution of LR and HR images is matched in a common latent space. Degradation information of the LR images and the high-frequency information of the HR images are fitted to an easy-to-sample conditional distribution. Experimental results on real-world image SR datasets indicate that SDFlow can generate diverse realistic LR and SR images both quantitatively and qualitatively.