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2014, JISOM
Although the technology of optical instruments is constantly advancing, the capture of high resolution images is limited by both the shortcoming s of the imaging devices and the law of physics (uncertainty principle applied onto photons or the wave like theory of light). The current paper presents an algorithm for processing a set of images sharing the same subject with the purpose of extracting a higher resolution output image of the subject, using partial information from every one of the low resolutions samples in the input set.
Theoretical and practical limitations usually constrain the achievable resolution of any imaging device. Super-Resolution (SR) methods are developed through the years to go beyond this limit by acquiring and fusing several low-resolution (LR) images of the same scene, producing a high-resolution (HR) image. The early works on SR, although occasionally mathematically optimal for particular models of data and noise, produced poor results when applied to real images. In this paper, we discuss two of the main issues related to designing a practical SR system, namely reconstruction accuracy and computational efficiency. Reconstruction accuracy refers to the problem of designing a robust SR method applicable to images from different imaging systems. We study a general framework for optimal reconstruction of images from grayscale, color, or color filtered (CFA) cameras. The performance of our proposed method is boosted by using powerful priors and is robust to both measurement (e.g. CCD re...
This paper deals with the problem of reconstructing High Resolution (HR) still image from a set of displaced, undersampled, and blurred measured images. It proposes an algorithm that uses the affine block-based algorithm in the maximum likelihood estimator. It is tested using synthetic Grayscale and Mono_Color images, where the reconstructed image can be compared with its original. A number of experiments were performed with the proposed algorithm over different sets of Low Resolution (LR) images to evaluate its behavior as a function of the number of available LR images. All the simulations correspond to synthetic data, in order to bypass problems such as translation estimation between measurements, and the blurring function estimation. The proposed algorithm accurately recovers the HR image even in the case where just very few input images are provided.
Subject identification from surveillance imagery has become an important task for forensic investigation. Good quality images of the subjects are essential for the surveillance footage to be useful. However, surveillance videos are of low resolution due to data storage requirements. In addition, subjects typically occupy a small portion of a camera's field of view. Faces, which are of primary interest, occupy an even smaller array of pixels. For reliable face recognition from surveillance video, there is a need to generate higher resolution images of the subject's face from low-resolution video. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. Super-resolution imaging (SR) is a class of techniques that enhance the resolution of an imaging system. Super-resolution image reconstruction is a signal processing based approach that aims to reconstruct a high-resolution image by combining a number of low-resolution images. Super-resolution improves image fidelity, and hence should improve the ability to distinguish between faces and consequently automatic face recognition accuracy.
2000 •
We present a definition and analysis of the concept of resolution and its enhancement in imaging based on statistical detection and estimation. We also present an overview of the problem of Super-resolution in imaging, which (similar to notions in MIMO communications) involves the reconstruction of high resolution images from a collection of "diverse" views of the same scene captured either
2017 •
Super-resolution is the process of recovering a high-resolution image from multiple low-resolution images of the same scene. The key objective of super-resolution (SR) imaging is to reconstruct a higher-resolution image based on a set of images, acquired from the same scene and denoted as ‘low-resolution’ images, to overcome the limitation and/or ill-posed conditions of the image acquisition process for facilitating better content visualization and scene recognition. In this paper, we provide a comprehensive review of existing super-resolution techniques and highlight the future research challenges. This includes the formulation of an observation model and coverage of the dominant algorithm – Iterative back projection .We critique these methods and identify areas which promise performance improvements. In this paper, future directions for super-resolution algorithms are discussed. Finally results of available methods are given.
Super-resolution is the process of converting many similar low-resolution images or video sequences. Super-resolution can be used for superior automatic target or object detection. There is always an uncertainty related to this method. An uncertainty is rarely considered during analysis. In many cases, uncertainties are the noisy signals and their processing leads to invalid results. This paper reviews certain super-resolution methodologies and presents two uncertainty model based super-resolution techniques. The presented models consider the uncertainty factor and establish an absolute probability distribution. The first model is based on analytical computation while the second is based on a statistical algorithm. These algorithms are presented as separate methods for image analysis and enhance performance compared with that of the existing automatic object recognition super-resolution methods.
Imaging plays a key role in many diverse areas of application, such as astronomy, remote sensing, microscopy, and tomography. Owing to imperfections of measuring devices (e.g., optical degradations, limited size of sensors) and instability of the observed scene (e.g., object motion, media turbulence), acquired images can be indistinct, noisy, and may exhibit insufficient spatial and temporal resolution. Super-Resolution (SR) image reconstruction is a promising technique of digital imaging which attempts to reconstruct High Resolution (HR) imagery by fusing the partial information contained within a number of under-sampled low-resolution (LR) images of that scene during the image reconstruction process. Super-resolution image reconstruction involves up-sampling of under-sampled images thereby filtering out distortions such as noise and blur. In comparison to various image enhancement techniques, super-resolution image reconstruction technique not only improves the quality of under-sa...
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