Convolutional Neural Network Based Models for Improving Super-Resolution Imaging (original) (raw)
Related papers
Deep Learning Based Approach Implemented to Image Super-Resolution
Journal of Advances in Information Technology
The aim of this research is about application of deep learning approach to the inverse problem, which is one of the most popular issues that has been concerned for many years about, the image Super-Resolution (SR). From then on, many fields of machine learning and deep learning have gained a lot of momentum in solving such imaging problems. In this article, we review the deep-learning techniques for solving the image super-resolution especially about the Generative Adversarial Network (GAN) technique and discuss other ways to use the GAN for an efficient solution on the task. More specifically, we review about the Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) and Residual in Residual Dense Network (RRDN) that are introduced by 'idealo' team and evaluate their results for image SR, they had generated precise results that gained the high rank on the leader board of state-of-the-art techniques with many other datasets like Set5, Set14 or DIV2K, etc. To be more specific, we will also review the Single-Image Super-Resolution using Generative Adversarial Network (SRGAN) and the Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN), two famous state-of-the-art techniques, by retrain the proposed model with different parameter and comparing with their result. So that can be helping us understand the working of announced model and the different when we choose others parameter compared to theirs.
Image Super-Resolution using Convolutional Neural Networks
International Journal of Advanced Research in Science, Communication and Technology, 2024
Image super-resolution is the process of enhancing the resolution of an image, typically from a lower resolution input to a higher resolution output. This research aims to explore the application of convolutional neural networks (CNNs) for image super-resolution. Specifically, the study will focus on developing a deep learning model capable of generating high-resolution images from low-resolution inputs. Various CNN architectures, such as SRCNN (Super-Resolution Convolutional Neural Network) or SRGAN (Super-Resolution Generative Adversarial Network), will be investigated and compared for their effectiveness in producing visually pleasing and perceptually accurate high-resolution images. Additionally, techniques such as residual learning, attention mechanisms, and adversarial training may be incorporated to further improve the quality of super-resolved images. The performance of the proposed models will be evaluated using standard image quality metrics and subjective assessments. This research has practical applications in enhancing the visual quality of low-resolution images in fields such as medical imaging, surveillance, and entertainment.
An Overview on Deep Learning in Image Super-Resolution for Advanced Machine Vision System
Proceedings of the 3rd International Conference on Integrated Intelligent Computing Communication & Security (ICIIC 2021), 2021
Image spatial resolution means the ability of the sensor to measure the smallest pixel size object. It focuses on recovering a less resolution (LR) image to high resolution (HR) image observations. Due to the exceptional research and application realizations of machine learning at home and abroad, the implementation effect of machine learning algorithms in image super resolution will be enormous. For this, deep learning has become a powerful learning tool for computer vision works. Furthermore, the performance of image super-resolution methods is showing significantly improved by using deep learning. In this paper, the basic image super-resolution methods based on deep learning have been discussed in detail along with the latest applications using super-resolution techniques. In addition, the current open issues and challenges for future research work are discussed. Finally, the main application areas of image superresolution based on deep learning domain are presented.
Single-Image Super Resolution Using Convolutional Neural Network
Procedia Computer Science, 2021
Increasing threats to U.S. national security satellite constellations have resulted in an increased interest in constellation resilience and satellite redundancy. CubeSats have contributed to commercial, scientific and government applications in remote sensing, communications, navigation and research and have the potential to enhance satellite constellation resilience. However, the inherent size, weight and power limitations of CubeSats enforce constraints on imaging hardware; the small lenses and short focal lengths result in imagery with low spatial resolution. Low resolution limits the utility of CubeSat images for military planning purposes and national intelligence applications. This paper implements a super-resolution deep learning architecture and proposes potential applications to CubeSat imagery.
ArXiv, 2020
In this work, very deep super-resolution (VDSR) method is presented for improving the spatial resolution of remotely sensed (RS) images for scale factor 4. The VDSR net is re-trained with Sentinel-2 images and with drone aero orthophoto images, thus becomes RS-VDSR and Aero-VDSR, respectively. A novel loss function, the Var-norm estimator, is proposed in the regression layer of the convolutional neural network during re-training and prediction. According to numerical and optical comparisons, the proposed nets RS-VDSR and Aero-VDSR can outperform VDSR during prediction with RS images. RS-VDSR outperforms VDSR up to 3.16 dB in terms of PSNR in Sentinel-2 images.
Image Super-Resolution using Generative Adversarial Networks with EfficientNetV2
International Journal of Advanced Computer Science and Applications
The image super-resolution is utilized for the image transformation from low resolution to higher resolution to obtain more detailed information to identify the targets. The superresolution has potential applications in various domains, such as medical image processing, crime investigation, remote sensing, and other image-processing application domains. The goal of the super-resolution is to obtain the image with minimal mean square error with improved perceptual quality. Therefore, this study introduces the perceptual loss minimization technique through efficient learning criteria. The proposed image reconstruction technique uses the image super-resolution generative adversarial network (ISRGAN), in which the learning of the discriminator in the ISRGAN is performed using the EfficientNet-v2 to obtain a better image quality. The proposed ISRGAN with the EfficientNet-v2 achieved a minimal loss of 0.02, 0.1, and 0.015 at the generator, discriminator, and self-supervised learning, respectively, with a batch size of 32. The minimal mean square error and mean absolute error are 0.001025 and 0.00225, and the maximal peak signal-to-noise ratio and structural similarity index measure obtained are 45.56985 and 0.9997, respectively.
Applied Sciences, 2020
In this paper, we propose a deep learning method with convolutional neural networks (CNNs) using skip connections with layer groups for super-resolution image reconstruction. In the proposed method, entire CNN layers for residual data processing are divided into several layer groups, and skip connections with different multiplication factors are applied from input data to these layer groups. With the proposed method, the processed data in hidden layer units tend to be distributed in a wider range. Consequently, the feature information from input data is transmitted to the output more robustly. Experimental results show that the proposed method yields a higher peak signal-to-noise ratio and better subjective quality than existing methods for super-resolution image reconstruction.
DeepSR: A deep learning tool for image super resolution
SoftwareX, 2022
An open source tool is introduced that provides a versatile environment to meet the needs of researchers in developing deep learning (DL) algorithms for single image super-resolution reconstruction (SISR). The processes of SISR were carefully studied, unified and integrated to create software that can be used by the community for any type of imaging method such as aerial, medical, optical, etc. DeepSR allows easy implementation of SISR application with rapidly prototyped DL models, and detailed reporting and recording of the results. The entire experiment can be done with simple command line scripts. It can be easily extended by user-defined metrics, augmentations, callbacks, etc. to cite Temiz, H. (2023). DeepSR: A deep learning tool for image super resolution. SoftwareX, 21, 101261. https://www.sciencedirect.com/science/article/pii/S2352711022001790?via%3Dihub
Single-frame super resolution of remote-sensing images by convolutional neural networks
International Journal of Remote Sensing, 2018
Super resolution (SR) refers to generation of a high-resolution (HR) image from a decimated, blurred, low-resolution (LR) image set, which can be either a single-frame or multi-frame that contains a collection of images acquired from slightly different views of the same observation area. In this study, two convolutional neural network (CNN)-based deep learning techniques are adapted in single-frame SR to increase the resolution of remote sensing (RS) images by a factor of 2, 3, and 4. In order to both preserve the colour information and speed up the algorithm, first an intensity hue saturation (IHS) transform is utilized and the SR techniques are only applied to the intensity channel of the images. Colour information is then restored with an inverse IHS transformation. We demonstrate the results of the proposed method on RS images acquired from Satellites Pour l'Observation de la Terre (SPOT) or Earth-observing satellites and Pleiades satellites with different spatial resolution. First synthetic LR images are created by downsampling, then structural similarity (SSIM) Index, peak signal-to-noise ratio (PSNR), Spectral Angle Mapper (SAM) and Erreur Relative Globale Adimensionnelle de Synthese (ERGAS) values are calculated for a quantitative evaluation of the methods. Finally, the method, with better performance results, is tested within a real scenario, that is, with original LR images as the input. The obtained HR images demonstrated visible qualitative enhancements.