1,497 research outputs found

    Focusing on out-of-focus : assessing defocus estimation algorithms for the benefit of automated image masking

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    Acquiring photographs as input for an image-based modelling pipeline is less trivial than often assumed. Photographs should be correctly exposed, cover the subject sufficiently from all possible angles, have the required spatial resolution, be devoid of any motion blur, exhibit accurate focus and feature an adequate depth of field. The last four characteristics all determine the " sharpness " of an image and the photogrammetric, computer vision and hybrid photogrammetric computer vision communities all assume that the object to be modelled is depicted " acceptably " sharp throughout the whole image collection. Although none of these three fields has ever properly quantified " acceptably sharp " , it is more or less standard practice to mask those image portions that appear to be unsharp due to the limited depth of field around the plane of focus (whether this means blurry object parts or completely out-of-focus backgrounds). This paper will assess how well-or ill-suited defocus estimating algorithms are for automatically masking a series of photographs, since this could speed up modelling pipelines with many hundreds or thousands of photographs. To that end, the paper uses five different real-world datasets and compares the output of three state-of-the-art edge-based defocus estimators. Afterwards, critical comments and plans for the future finalise this paper

    Fast and easy blind deblurring using an inverse filter and PROBE

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    PROBE (Progressive Removal of Blur Residual) is a recursive framework for blind deblurring. Using the elementary modified inverse filter at its core, PROBE's experimental performance meets or exceeds the state of the art, both visually and quantitatively. Remarkably, PROBE lends itself to analysis that reveals its convergence properties. PROBE is motivated by recent ideas on progressive blind deblurring, but breaks away from previous research by its simplicity, speed, performance and potential for analysis. PROBE is neither a functional minimization approach, nor an open-loop sequential method (blur kernel estimation followed by non-blind deblurring). PROBE is a feedback scheme, deriving its unique strength from the closed-loop architecture rather than from the accuracy of its algorithmic components

    Image blur estimation based on the average cone of ratio in the wavelet domain

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    In this paper, we propose a new algorithm for objective blur estimation using wavelet decomposition. The central idea of our method is to estimate blur as a function of the center of gravity of the average cone ratio (ACR) histogram. The key properties of ACR are twofold: it is powerful in estimating local edge regularity, and it is nearly insensitive to noise. We use these properties to estimate the blurriness of the image, irrespective of the level of noise. In particular, the center of gravity of the ACR histogram is a blur metric. The method is applicable both in case where the reference image is available and when there is no reference. The results demonstrate a consistent performance of the proposed metric for a wide class of natural images and in a wide range of out of focus blurriness. Moreover, the proposed method shows a remarkable insensitivity to noise compared to other wavelet domain methods

    Improvement of Spatial Resolution with Staggered Arrays As Used in The Airborne Optical Sensor Ads40

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    Using pushbroom sensors onboard aircrafts or satellites requires, especially for photogrammetric applications, wide image swaths with a high geometric resolution. One approach to satisfy both demands is to use staggered line arrays, which are constructed from two identical CCD lines shifted against each other by half a picel in line direction. Practical applications of such arrays in remote sensing include SPOT, and in the commercial environment the Airborne Digital Sensor, or ADS40, from Leica Geosystems. Theoretically, the usefulness of staggered arrays depends from spatial reslution, which is defined by the total point spread function of the imaging system and Shannon's sampling theorem. Due to the two shifted sensor lines staggering results in a double number of sampling points perpendicular to the flight direction. In order to simultaneously double the sample number in the flight direction, the line readout rate, or integration time, has to produce half a pixel spacing on ground. Staggering in combination with a high-resolution optical system can be used to fulfil the sampling condition, which means that no spectral components above the critical spatial frequency 2/D are present. Theoretically, the resolution is as good for a non-staggered line with half pixel size D/2, but radiometric dynamics should be twice as high. In practice, the slightly different viewing angle of both lines of a staggered array can result in a deteration of image quality due to aircraft motion, attitude fluctuations or terrain undulation. Fulfilling the sampling condition further means that no aliasing occurs. This is essential for the image quality in quasiperiodical textured image areas and for photogrammetric sub-pixel accuracy. Furthermore, image restoration methods for enhancing the image quality can be applied more efficently. The panchromatic resolution of the ADS40 opties is optimised for image collection by a staggered array. This means, it transfers spatial frequencies of twice the Nyquist frequency of its 12k sensors. First experiments, which were carried out some years ago, indicated alrady a spatial resolution improvement by using image restitution the ADS 40 staggered 12k pairs. The results of the restitution algorithm, which is integrated in the ADS image processing flow, has now been analysed quantitatively. This paper presents the theory of high resolution image restitution from staggered lines and practical results with ADS40 high resolution panchromatic images and high resolution colour images, created by sharpening 12k colour images with high resolution pan-chromatic ones

    A new Edge Detector Based on Parametric Surface Model: Regression Surface Descriptor

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    In this paper we present a new methodology for edge detection in digital images. The first originality of the proposed method is to consider image content as a parametric surface. Then, an original parametric local model of this surface representing image content is proposed. The few parameters involved in the proposed model are shown to be very sensitive to discontinuities in surface which correspond to edges in image content. This naturally leads to the design of an efficient edge detector. Moreover, a thorough analysis of the proposed model also allows us to explain how these parameters can be used to obtain edge descriptors such as orientations and curvatures. In practice, the proposed methodology offers two main advantages. First, it has high customization possibilities in order to be adjusted to a wide range of different problems, from coarse to fine scale edge detection. Second, it is very robust to blurring process and additive noise. Numerical results are presented to emphasis these properties and to confirm efficiency of the proposed method through a comparative study with other edge detectors.Comment: 21 pages, 13 figures and 2 table
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