International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 09 Issue: 05 | May 2022
p-ISSN: 2395-0072
www.irjet.net
Image super resolution using Generative Adversarial Network. Vinaya Akhade1, Rutuja Naikwade2, Harshada Sherkar3, Mrs. Asmita R. Kamble 4 1,2,3,4 B.Tech student, Computer Science, Sinhgad Institute of Technology and Science , Pune, India ---------------------------------------------------------------------------***--------------------------------------------------------------------------
Abstract -
Super-resolution (SR) is an image processing technique that aims to increase the resolution of an image by adding sub- pixel detail. The information used for adding detail can come from sub-pixel shifts provided by sequences of images (frequency domain), or by a good understanding of the degradation processes, including blurring, that cause the loss of detail. Convolutional neural networks (CNNs) are especially suited for this type of application due to their ability to empirically map the underlying connections between an image pixel and those surrounding it. Conversion from multiple low resolution (LR) images to high resolution (HR) image is done by using super-resolution techniques. Anyone can achieve more information in detail from high- resolution images, which helps further for many satellite image applications. This growing technology interest in the reconstruction of imagery leads to several methodologies in the field of advanced digital color image processing. Recent years have seen growing interest in the problem of super-resolution restoration of video sequences. Whereas in the traditional single image restoration problem only a single input image is available for processing, the task of reconstructing super-resolution images from multiple under sampled and degraded images can take advantage of the additional patiotemporal data available in the image sequence. In particular, camera and scene motion lead to frames in the source video sequence containing similar, but not identical information. The additional information available in these frames make possible reconstruction of visually superior frames at higher resolution than that of the original data.
Key Words: Convolutional Neural Network (CNN), Super-Resolution (SR), High-resolution (HR) , Low resolution (LR)
1. INTRODUCTION Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision. Recent years have witnessed remarkable progress of image super-resolution using deep learning tech- niques. Super resolution is the process of combining a sequence of low-resolution (LR) noisy blurred images to produce a higher resolution image or sequence. Super- resolution of image is the most widely used and extensive area of research. The resolution is referred as an important aspect of image. The problem of limited reso- lution by image acquisition devices can be solved by super resolution. Image super-resolution (SR), which refers to the process of recovering high-resolution (HR) images from low resolution (LR) images, is an important class of image pro- cessing techniques in computer vision and image processing. It enjoys a wide range of real-world applications, such as medical imaging, surveillance and security , amongst others. Other than improving image perceptual quality, it also helps to improve other computer vision tasks. In general, this problem is very challenging and inherently ill-posed since there are always multiple HR images corresponding to a single LR image. In literature, a variety of classical SR methods have been pro- posed, including prediction-based methods , edge-based methods ,statistical methods, patch-based methods and sparse representation methods etc. The main contributions of this survey are three-fold: 1) We give a comprehensive review of image super resolution techniques based on deep learning, including problem settings, benchmark datasets, performance metrics, a family of SR methods with deep learning, domain-specific SR applications, etc. 2) We provide a systematic overview of recent advances of deep learning based SR techniques in a hierarchical and structural manner, and summarize the advantages
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