scholarly journals Learning Based Resolution Enhancement of Digital Images

2019 ◽  
Vol 8 (6) ◽  
pp. 3026-3030
Keyword(s):  
Digital Images ◽  
Signal To Noise Ratio ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Image Resolution ◽  
Test Dataset ◽  
Analysis Process ◽  
Image Super Resolution ◽  
Leaky Rectified Linear Unit

Image super-resolution (SR), the process that improves the resolution, has been used in many real world applications. SR is the preprocessing phase of majority of these applications. The improvement in image resolution improves the performance of image analysis process. The SR of digital images take the low resolution images as inputs. In this article, a learning based digital image SR approach is proposed. The proposed approach uses Convolutional Neural Network (CNN) with leaky rectified linear unit (ReLU) for learning and generalization. The experiments with the test dataset from USC-SIPI indicate that the proposed approach increases the quality of the images in terms of the quantitative metric peak signal to noise ratio. Further, it avoided the problem of dying ReLU.

scholarly journals Super-Resolution of Digital Images using CNN with Leaky ReLU

2019 ◽  
Vol 8 (2S11) ◽  
pp. 210-212 ◽  
Keyword(s):  
Digital Images ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Image Resolution ◽  
Signal To Noise ◽  
Analysis Process ◽  
Real World Applications ◽  
Image Super Resolution ◽  
Leaky Rectified Linear Unit

Image super-resolution (SR) has been used in many real world applications as a preprocessing phase. The improvement in image resolution increases the performance of image analysis process. The SR of digital images is achieved by taking the low resolution images as inputs. In this article, a novel deeplearning based super-resolution approach is proposed. The proposed approach uses Convolutional Neural Network (CNN) with leaky rectified linear unit (ReLU) for learning and generalization. The experiments with test images taken from USC-SIPI dataset indicate that the proposed approach increases the quality of the images in terms of the quantitative metric peak signal to noise ratio.

scholarly journals Small Object Detection in Remote Sensing Images with Residual Feature Aggregation-Based Super-Resolution and Object Detector Network

2021 ◽  
Vol 13 (9) ◽  
pp. 1854
Author(s):  
Syed Muhammad Arsalan Bashir ◽  
Yi Wang
Keyword(s):  
Remote Sensing ◽  
Object Detection ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Detection Performance ◽  
Image Resolution ◽  
Small Object ◽  
Remote Sensing Images ◽  
Feature Aggregation ◽  
Image Super Resolution

This paper deals with detecting small objects in remote sensing images from satellites or any aerial vehicle by utilizing the concept of image super-resolution for image resolution enhancement using a deep-learning-based detection method. This paper provides a rationale for image super-resolution for small objects by improving the current super-resolution (SR) framework by incorporating a cyclic generative adversarial network (GAN) and residual feature aggregation (RFA) to improve detection performance. The novelty of the method is threefold: first, a framework is proposed, independent of the final object detector used in research, i.e., YOLOv3 could be replaced with Faster R-CNN or any object detector to perform object detection; second, a residual feature aggregation network was used in the generator, which significantly improved the detection performance as the RFA network detected complex features; and third, the whole network was transformed into a cyclic GAN. The image super-resolution cyclic GAN with RFA and YOLO as the detection network is termed as SRCGAN-RFA-YOLO, which is compared with the detection accuracies of other methods. Rigorous experiments on both satellite images and aerial images (ISPRS Potsdam, VAID, and Draper Satellite Image Chronology datasets) were performed, and the results showed that the detection performance increased by using super-resolution methods for spatial resolution enhancement; for an IoU of 0.10, AP of 0.7867 was achieved for a scale factor of 16.

scholarly journals HAADF-STEM Image Resolution Enhancement Using High-Quality Image Reconstruction Techniques: Case of the Fe3O4(111) Surface

2019 ◽  
Vol 25 (6) ◽  
pp. 1297-1303
Author(s):  
G. Bárcena-González ◽  
M. P. Guerrero-Lebrero ◽  
E. Guerrero ◽  
A. Yañez ◽  
B. Nuñez-Moraleda ◽  
...  
Keyword(s):  
Signal To Noise Ratio ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
Dark Field ◽  
Image Resolution ◽  
Computational Techniques ◽  
Cross Sectional ◽  
Microscopy Imaging ◽  
Image Reconstruction Techniques ◽  
Scanning Transmission

AbstractFrom simple averaging to more sophisticated registration and restoration strategies, such as super-resolution (SR), there exist different computational techniques that use a series of images of the same object to generate enhanced images where noise and other distortions have been reduced. In this work, we provide qualitative and quantitative measurements of this enhancement for high-angle annular dark-field scanning transmission electron microscopy imaging. These images are compared in two ways, qualitatively through visual inspection in real and reciprocal space, and quantitatively, through the calculation of objective measurements, such as signal-to-noise ratio and atom column roundness. Results show that these techniques improve the quality of the images. In this paper, we use an SR methodology that allows us to take advantage of the information present in the image frames and to reliably facilitate the analysis of more difficult regions of interest in experimental images, such as surfaces and interfaces. By acquiring a series of cross-sectional experimental images of magnetite (Fe3O4) thin films (111), we have generated interpolated images using averaging and SR, and reconstructed the atomic structure of the very top surface layer that consists of a full monolayer of Fe, with topmost Fe atoms in tetrahedrally coordinated sites.

scholarly journals Sparse Representation Based Super-Resolution Algorithm using Wavelet Domain Interpolation and Nonlocal Means

2015 ◽  
Vol 16 (2) ◽  
pp. 296
Author(s):  
Gunnam Suryanarayana ◽  
Ravindra Dhuli
Keyword(s):  
Resolution Enhancement ◽  
Super Resolution ◽  
Image Resolution ◽  
Sparse Signal Recovery ◽  
Signal Recovery ◽  
Nonlocal Means ◽  
Image Super Resolution ◽  
Resolution Algorithm ◽  
Single Image Super Resolution ◽  
Enhancement Algorithm

In this correspondence, we propose a novel image resolution enhancement algorithm based on discretewavelet transform (DWT), stationary wavelet transform (SWT) and sparse signal recovery of the inputimage. The nonlocal means filter is employed in the preliminary denoising stage of the proposed method.The denoised input low resolution (LR) image is then decomposed into different frequency subbands byemploying DWT and SWT simultaneously. In parallel, the denoised LR image is subjected to a sparse signalrepresentation based interpolation. All the estimated high frequency subbands as well as the sparseinterpolated LR image are fused to generate a high resolution (HR) image by using inverse discrete wavelettransform (IDWT). Experimental results on various test images show the superiority of our method over theconventional and state-of-the-art single image super- resolution (SR) techniques in achieving the real timeperformance.

scholarly journals Learning Weighted Forest and Similar Structure for Image Super Resolution

2019 ◽  
Vol 9 (3) ◽  
pp. 543 ◽  
Author(s):  
Ziwei Lu ◽  
Chengdong Wu ◽  
Xiaosheng Yu
Keyword(s):  
Clustering Algorithm ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Low Rank ◽  
Training Dataset ◽  
Image Patches ◽  
Non Local ◽  
External Training ◽  
Image Super Resolution

Image super resolution (SR) based on example learning is a very effective approach to achieve high resolution (HR) image from image input of low resolution (LR). The most popular method, however, depends on either the external training dataset or the internal similar structure, which limits the quality of image reconstruction. In the paper, we present a novel SR algorithm by learning weighted random forest and non-local similar structures. The initial HR image patches are obtained from a weighted forest model, which is established by calculating the approximate fitting error of the leaf nodes. The K-means clustering algorithm is exploited to get a non-local similar structure inside the initial HR image patches. In addition, a low rank constraint is imposed on the HR image patches in each cluster. We further apply the similar structure model to establish an effective regularization prior under a reconstruction-based SR framework. Comparing with current typical SR algorithms, the results of comprehensive experiments implemented on three publicly datasets show that peak signal-to-noise ratio (PSNR) has been effectively promoted by the presented SR approach, and a better visual effect has been realized.

Contribution of enhanced DWT and non-negative structured sparse representation for image super resolution: A performance analysis

2020 ◽  
Vol 18 (06) ◽  
pp. 2050049
Author(s):  
A. Valli Bhasha ◽  
B. D. Venkatramana Reddy
Keyword(s):  
Sparse Representation ◽  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Resolution ◽  
Discrete Wavelet ◽  
Spotted Hyena ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Two Phases

The image super-resolution methods with deep learning using Convolutional Neural Network (CNN) have been producing admirable advancements. The proposed image resolution model involves the following two main analyses: (i) analysis using Adaptive Discrete Wavelet Transform (ADWT) with Deep CNN and (ii) analysis using Non-negative Structured Sparse Representation (NSSR). The technique termed as NSSR is used to recover the high-resolution (HR) images from the low-resolution (LR) images. The experimental evaluation involves two phases: Training and Testing. In the training phase, the information regarding the residual images of the dataset are trained using the optimized Deep CNN. On the other hand, the testing phase helps to generate the super resolution image using the HR wavelet subbands (HRSB) and residual images. As the main novelty, the filter coefficients of DWT are optimized by the hybrid Fire Fly-based Spotted Hyena Optimization (FF-SHO) to develop ADWT. Finally, a valuable performance evaluation on the two benchmark hyperspectral image datasets confirms the effectiveness of the proposed model over the existing algorithms.

scholarly journals Shuffle block SRGAN for face image super-resolution reconstruction

2020 ◽  
Vol 53 (7-8) ◽  
pp. 1429-1439
Author(s):  
Ziwei Zhang ◽  
Yangjing Shi ◽  
Xiaoshi Zhou ◽  
Hongfei Kan ◽  
Juan Wen
Keyword(s):  
Face Recognition ◽  
Learning Strategy ◽  
Signal To Noise Ratio ◽  
Similarity Index ◽  
Super Resolution ◽  
Face Image ◽  
Low Resolution ◽  
Index Method ◽  
Face Features ◽  
Image Super Resolution

When low-resolution face images are used for face recognition, the model accuracy is substantially decreased. How to recover high-resolution face features from low-resolution images precisely and efficiently is an essential subtask in face recognition. In this study, we introduce shuffle block SRGAN, a new image super-resolution network inspired by the SRGAN structure. By replacing the residual blocks with shuffle blocks, we can achieve efficient super-resolution reconstruction. Furthermore, by considering the generated image quality in the loss function, we can obtain more realistic super-resolution images. We train and test SB-SRGAN in three public face image datasets and use transfer learning strategy during the training process. The experimental results show that shuffle block SRGAN can achieve desirable image super-resolution performance with respect to visual effect as well as the peak signal-to-noise ratio and structure similarity index method metrics, compared with the performance attained by the other chosen deep-leaning models.

Gaussian Noised Single-Image Super Resolution Reconstruction

2013 ◽  
Vol 457-458 ◽  
pp. 1032-1036
Author(s):  
Feng Qing Qin ◽  
Li Hong Zhu ◽  
Li Lan Cao ◽  
Wa Nan Yang
Keyword(s):  
Gaussian Noise ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Projection Algorithm ◽  
Single Image ◽  
Low Resolution ◽  
Filtering Algorithm ◽  
Resolution Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

A framework is proposed to reconstruct a super resolution image from a single low resolution image with Gaussian noise. The degrading processes of Gaussian blur, down-sampling, and Gaussian noise are all considered. For the low resolution image, the Gaussian noise is reduced through Wiener filtering algorithm. For the de-noised low resolution image, iterative back projection algorithm is used to reconstruct a super resolution image. Experiments show that de-noising plays an important part in single-image super resolution reconstruction. In the super reconstructed image, the Gaussian noise is reduced effectively and the peak signal to noise ratio (PSNR) is increased.

scholarly journals Research on Image Denoising and Super-Resolution Reconstruction Technology of Multiscale-Fusion Images

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Size Li ◽  
Pengjiang Qian ◽  
Xin Zhang ◽  
Aiguo Chen
Keyword(s):  
Image Processing ◽  
Receptive Field ◽  
Image Denoising ◽  
State Of The Art ◽  
Experimental Studies ◽  
Super Resolution ◽  
Image Resolution ◽  
Reconstruction Method ◽  
Model Learning ◽  
Image Super Resolution

Image denoising and image super-resolution reconstruction are two important techniques for image processing. Deep learning is used to solve the problem of image denoising and super-resolution reconstruction in recent years, and it usually has better results than traditional methods. However, image denoising and super-resolution reconstruction are studied separately by state-of-the-art work. To optimally improve the image resolution, it is necessary to investigate how to integrate these two techniques. In this paper, based on Generative Adversarial Network (GAN), we propose a novel image denoising and super-resolution reconstruction method, i.e., multiscale-fusion GAN (MFGAN), to restore the images interfered by noises. Our contributions reflect in the following three aspects: (1) the combination of image denoising and image super-resolution reconstruction simplifies the process of upsampling and downsampling images during the model learning, avoiding repeated input and output images operations, and improves the efficiency of image processing. (2) Motivated by the Inception structure and introducing a multiscale-fusion strategy, our method is capable of using the multiple convolution kernels with different sizes to expand the receptive field in parallel. (3) The ablation experiments verify the effectiveness of each employed loss measurement in our devised loss function. And our experimental studies demonstrate that the proposed model can effectively expand the receptive field and thus reconstruct images with high resolution and accuracy and that the proposed MFGAN method performs better than a few state-of-the-art methods.

scholarly journals Evaluation of sted super-resolution image quality by image correlation spectroscopy (QuICS)

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Elena Cerutti ◽  
Morgana D’Amico ◽  
Isotta Cainero ◽  
Gaetano Ivan Dellino ◽  
Mario Faretta ◽  
...  
Keyword(s):  
Autocorrelation Function ◽  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Image Contrast ◽  
Correlation Spectroscopy ◽  
Image Correlation ◽  
Signal To Noise ◽  
Image Correlation Spectroscopy ◽  
Noise Ratio

AbstractQuantifying the imaging performances in an unbiased way is of outmost importance in super-resolution microscopy. Here, we describe an algorithm based on image correlation spectroscopy (ICS) that can be used to assess the quality of super-resolution images. The algorithm is based on the calculation of an autocorrelation function and provides three different parameters: the width of the autocorrelation function, related to the spatial resolution; the brightness, related to the image contrast; the relative noise variance, related to the signal-to-noise ratio of the image. We use this algorithm to evaluate the quality of stimulated emission depletion (STED) images of DNA replication foci in U937 cells acquired under different imaging conditions. Increasing the STED depletion power improves the resolution but may reduce the image contrast. Increasing the number of line averages improves the signal-to-noise ratio but facilitates the onset of photobleaching and subsequent reduction of the image contrast. Finally, we evaluate the performances of two different separation of photons by lifetime tuning (SPLIT) approaches: the method of tunable STED depletion power and the commercially available Leica Tau-STED. We find that SPLIT provides an efficient way to improve the resolution and contrast in STED microscopy.

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