Superresolution in color videos acquired through turbulent media

Fishbain, Barak; Ideses, Ianir A.; Shabat, Gil; Salomon, Benjamin G.; Yaroslavsky, Leonid P.

doi:10.1364/ol.34.000587

Cited by 7 publications

(6 citation statements)

References 9 publications

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“…The result in Figures (4,6) show that the proposed algorithm is successful in producing sharp stabilized images with no noticeable artifacts. Due to space constraints , we have made other image sets available online * We evaluate the performance of the algorithm not only by the images produced, but by using quantitative measures of signal-to-noise-ratio (SNR) and sharpness using effective bandwidth B. Fishbain and L. Yaroslavsky 18 and blur metric by Fishbain et al 19 The latter is relatively simple and effective. These performance metrics are described in greater detail in R. Kakarala and R. Hebbalaguppe 14…”

Section: Hdr Resultsmentioning

confidence: 99%

An efficient multiple exposure image fusion in JPEG domain

Hebbalaguppe

Kakarala

2012

SPIE Proceedings

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In this paper, we describe a method to fuse multiple images taken with varying exposure times in the JPEG domain. The proposed algorithm finds its application in HDR image acquisition and image stabilization for hand-held devices like mobile phones, music players with cameras, digital cameras etc. Image acquisition at low light typically results in blurry and noisy images for hand-held camera's. Altering camera settings like ISO sensitivity, exposure times and aperture for low light image capture results in noise amplification, motion blur and reduction of depth-of-field respectively. The purpose of fusing multiple exposures is to combine the sharp details of the shorter exposure images with high signal-to-noise-ratio (SNR) of the longer exposure images. 1 The algorithm requires only a single pass over all images, making it efficient. It comprises of -sigmoidal boosting 2 of shorter exposed images, image fusion, artifact removal and saturation detection. Algorithm does not need more memory than a single JPEG macro block to be kept in memory making it feasible to be implemented as the part of a digital cameras hardware image processing engine. The Artifact removal step reuses the JPEG's built-in frequency analysis and hence benefits from the considerable optimization and design experience that is available for JPEG.

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Section: Hdr Resultsmentioning

confidence: 99%

An efficient multiple exposure image fusion in JPEG domain

Hebbalaguppe

Kakarala

2012

SPIE Proceedings

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“…Applications of these approaches to imitation of image retrieval through turbulent media (an illustration one can see at [ 120]) and to stabilization and superresolution of turbulent videos are presented in Refs. [ 121], [ 122] and [ 123].…”

Section: Image Resampling and Building "Continuous" Image Modelsmentioning

confidence: 99%

Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging

Yaroslavsky¹

2015

J. Opt.

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Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet and alike. They are the basic tool in image compression, in image restoration, in image resampling and geometrical transformations and can be traced back to early 1970-ths. The paper presents a review of these methods with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive transform domain filters for image restoration, to methods of precise image resampling and image reconstruction from sparse samples and up to "compressive sensing" approach that has gained popularity in last few years. The review has a tutorial character and purpose.

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“…In these cases, the perfect shifting filter can be applied to image fragments in sliding window for evaluating interpolated value of the window central pixel at each window position. Typical application examples are imitation of image retrieval through turbulent media (for illustration see [88]) and stabilization and superresolution of turbulent videos [89][90][91].…”

Section: Image Resampling and Building ''Continuous'' Image Modelsmentioning

confidence: 99%

Fast Transforms in Image Processing: Compression, Restoration, and Resampling

Yaroslavsky

2014

Advances in Electrical Engineering

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Transform image processing methods are methods that work in domains of image transforms, such as Discrete Fourier, Discrete Cosine, Wavelet, and alike. They proved to be very efficient in image compression, in image restoration, in image resampling, and in geometrical transformations and can be traced back to early 1970s. The paper reviews these methods, with emphasis on their comparison and relationships, from the very first steps of transform image compression methods to adaptive and local adaptive filters for image restoration and up to “compressive sensing” methods that gained popularity in last few years. References are made to both first publications of the corresponding results and more recent and more easily available ones. The review has a tutorial character and purpose.

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Superresolution in color videos acquired through turbulent media

Cited by 7 publications

References 9 publications

An efficient multiple exposure image fusion in JPEG domain

An efficient multiple exposure image fusion in JPEG domain

Compression, restoration, resampling, ‘compressive sensing’: fast transforms in digital imaging

Fast Transforms in Image Processing: Compression, Restoration, and Resampling

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