Iterative multiframe super-resolution algorithms for atmospheric turbulence-degraded imagery

Sheppard, D.G.; Hunt, B. R.; Marcellin, Michael W.

doi:10.1109/icassp.1998.678121

Cited by 8 publications

(9 citation statements)

References 8 publications

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“…Restoring images degraded by low speed airflow such as atmospheric turbulence was studied by many researchers a long time ago, and lots of papers about this topic were published [1]~ [4] , some of which gave good restoring effect. However, there is recently a great urgency for real-time restoring images degraded by high speed airflow, mainly from military applications such as the infrared imaging guidance high speed aircraft, whose optical system is affected badly by aero-optics effects while flying in thick atmosphere.…”

Section: Introductionmentioning

confidence: 99%

A real-time restoring method for infrared images degraded by high-speed airflow

Fei

Chen

2011

SPIE Proceedings

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Restoring images degraded by low speed airflow such as atmospheric turbulence was studied by many researchers a long time ago, and many methods were proposed. However, those methods proposed for low speed airflow generally can't meet this urgency mainly in two aspects: first, those methods are usually time-consuming, which fail time requisition; second, those deconvolution models designed for low speed airflow may not suit the case of high speed airflow, which leads to bad restoring effect. Because existent blind deconvolution methods are not competent for real-time restoring infrared images degraded by high speed airflow, a fast restoring method for that application is researched in this paper. Both the PSF estimation and the object estimation processes are constructed to improve the algorithm's efficiency. The simplified Weiner filter is adopted to fast estimate objects given PSF. For traditional methods, many computation methods, including iterative and non-iterative ones, can be used to resolve that problem. But we are more interested in non-iterative methods because of application background of our work, in which the speed of restoring algorithms is also an important factor that we must be concerned about. Therefore we choose here inverse-filtering method to estimate the object and use FFT to accelerate the computation. Gaussian-like function is used to approximate the PSF of degradation of high speed airflow. There are many papers on blur identification and was summarized well. However, those methods are either iterative, leading to slow estimation, or not feasible for Gaussian-like PSF. Therefore We figure out a new a frequency domain scheme to estimate the parameter of PSF quickly, and use both simulation and wind tunnel experiment infrared images to test its validation. Finally we compare our algorithm with other three blind algorithms, that is the Rechardson-Lucy method (RL), the maximum likelihood method (ML) and the primary component analysis method (PCA), and the results show that our algorithm not only gives much better result, but also consumes much less time.KEYWORD-image restoring, infrared image.

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Section: Introductionmentioning

confidence: 99%

A real-time restoring method for infrared images degraded by high-speed airflow

Fei

Chen

2011

SPIE Proceedings

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“…Today, advances in computers with higher accuracy and processing power, has caused more attention to be software based super-resolution methods. super-resolution is used in monitoring systems such as the identification and recognition of license plates [1], face recognition [2], automatic target recognition [3] and [4], remote sensing [5], medical image processing such as CT and MRI [6] and [7], converting video to different standards (for example, converting NTSC to HDTV), image enhancing [8], processing of satellite images [9], astronomical image processing [10], microscopic image processing [11] and image mosaicking [12].…”

Section: Introductionmentioning

confidence: 99%

A Survey on Super-Resolution Methods for Image Reconstruction

Karimi¹,

Kangarloo²,

Javadi³

2014

IJCA

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Today, in many applications of Machine vision, image SuperResolution is preferred. Super-Resolution is estimation of a high-resolution image from an image or several low resolution images. Popular techniques in the field of enhancing images can be used to remove noise or blurring. In this paper, an overview of super resolution methods has been presented. Types of resolution methods have been used so far can be divided into three groups as frequency-domain methods, spatial domain methods and techniques can be classified as the wavelet domain. Super-resolution methods in different domains have different characteristics and comparison between these methods is usually done using a special index in one domain. In this paper, we will introduce these indexes and review best techniques used in all three domains.

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“…Existing blind deconvolution methods can be categorized into two main classes: the methods which separate PSF identification as a disjoint process from restoration [5,6,7], and the methods which combine PSF identification and restoration in one procedure [8,9,10,11].…”

Section: Introductionmentioning

confidence: 99%

“…No criterion is employed to assess the best stepedge from the set of sharp edge elements in the degraded image. Methods in the second class use iterative methods to approximate the blur extent [3,4,[8][9][10][11]. They often formulate parametric models for both the image and the blur.…”

Section: Introductionmentioning

confidence: 99%

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Estimation of Image Corruption Inverse Function and Image Restoration using a PSObased Algorithm

Aghababa¹,

Shotorbani²,

Narimani³

2011

IJCA

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A new method is proposed to estimate corruption function inverse of a blurred image. This technique can be used for restoring similar corrupted images. For linear position invariant procedure, the corruption process is modeled in the spatial domain by convolving the image with a point spread function (PSF) and addition of some noises into the image. It is assumed that a given artificial image is corrupted by a degradation function, represented by the PSF, and an additive noise. Then a filter mask (as a candidate for the corruption function inverse) is calculated to restore the original image from the corrupted one, with some accuracy. Calculating a suitable filter mask is formulated as an optimization problem: find optimal coefficients of the filter mask such that the difference between the original image and filter mask restored image to be minimized. Particle swarm optimization (PSO) is used to compute the optimal coefficients of the filter mask. Square filter masks are considered. A comparison between different exciting methods and the proposed technique is done using simulations. The simulation results show that the proposed method is effective and efficient. Since the proposed method is a simple linear technique, it can be easily implemented in hardware or software.

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Iterative multiframe super-resolution algorithms for atmospheric turbulence-degraded imagery

Cited by 8 publications

References 8 publications

A real-time restoring method for infrared images degraded by high-speed airflow

A real-time restoring method for infrared images degraded by high-speed airflow

A Survey on Super-Resolution Methods for Image Reconstruction

Estimation of Image Corruption Inverse Function and Image Restoration using a PSObased Algorithm

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