Satellite and aerial image deconvolution using an EM method with complex wavelets

Jalobeanu, A.; Nowak, Robert; Zerubia, Josiane; Figueiredo, Mário A. T.

doi:10.1109/icip.2002.1038028

Cited by 6 publications

(2 citation statements)

References 11 publications

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“…Our implementation parallels the formulation in [6], except that the denoising M-step is actually performed directly in the time domain, since the time domain representation of the ideal signal (spike train) is already as sparse as possible. A favorable feature of this algorithm is the provision of a tunable parameter, τ, that directly controls the number of spikes in the output signal and, hence, the density of the resulting point cloud.…”

Section: Methodsmentioning

confidence: 99%

Exploiting full-waveform lidar data and multiresolution wavelet analysis for vertical object detection and recognition

Parrish¹

2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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Abstract-A current challenge in performing airport obstruction surveys using airborne lidar is lack of reliable, automated methods for extracting and attributing vertical objects from the lidar data. This paper presents a new approach to solving this problem, taking advantage of the additional data provided by full-waveform systems. The procedure entails first deconvolving and georeferencing the lidar waveform data to create dense, detailed point clouds in which the vertical structure of objects, such as trees, towers, and buildings, is well characterized. The point clouds are then voxelized to produce high-resolution volumes of lidar intensity values, and a 3D wavelet decomposition is computed. Vertical object detection and recognition is performed in the wavelet domain using a multiresolution template matching approach. The method was tested using lidar waveform data and ground truth collected for project areas in Madison, Wisconsin.Preliminary results demonstrate the potential of the approach.

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Section: Methodsmentioning

confidence: 99%

Exploiting full-waveform lidar data and multiresolution wavelet analysis for vertical object detection and recognition

Parrish¹

2007

2007 IEEE International Geoscience and Remote Sensing Symposium

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“…A few of these methods require the knowledge of the Point Spread Function (PSF), others use statistical information. Others recent approaches like in [15], gives good results, but are not suitable for real time application. Our work starts from consideration found in [13], [14], which propose complete blind restoration systems.…”

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confidence: 99%

Restoration of out-of-focus images based on circle of confusion estimate

et al. 2002

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In this paper a new method for a fast out-of-focus blur estimation and restoration is proposed. It is suitable for CFA (Color Filter Array) images acquired by typical CCD/CMOS sensor. The method is based on the analysis of a single image and consists of two steps: 1) out-of-focus blur estimation via Bayer pattern analysis; 2) image restoration. Blur estimation is based on a block-wise edge detection technique. This edge detection is carried out on the green pixels of the CFA sensor image also called Bayer pattern. Once the blur level has been estimated the image is restored through the application of a new inverse filtering technique. This algorithm gives sharp images reducing ringing and crisping artifact, involving wider region of frequency. Experimental results show the effectiveness of the method, both in subjective and numerical way, by comparison with other techniques found in literature..H\ZRUGV Bayer pattern, circle of confusion, blur, blind restoration, inverse filtering, out-of-focus ,1752'8&7,21In the consumer electronics world the tendency is to provide an increasing number of functionality in a single unit. As a result, a mobile phone can acquire pictures and send them as e-mail messages through the cellular network. The images are acquired by CCD/CMOS sensor and processed by typical processing technique (Image Generation Pipeline) as described in [1]. However, due to limited dimension and quality of the camera inside the phone, images are often degraded. An enhancement of the acquired images is necessary in order to make them pleasant to the human viewer [2][3]. Our work is aimed to restore slightly out-of-focus images taken through low quality camera and lens. In this paper a new method for simultaneous out-of-focus blur estimation and restoration based on a single image is proposed. The method consists of two steps: 1) out-of-focus blur estimation; 2) image restoration by a new inverse filtering technique. Out-of-focus blur estimation and image restoration are two different image-processing problems. Blur estimation is used both for depth perception from two differently focused images of the same scene [4] and to estimate the out-of-focus blur of an image [5] [6]. The image restoration problem has been faced in several ways. NAS-RIF [7], for example, involves minimizing a cost function while NLIVQ (nonlinear interpolative vector quantization) [8] and ARMA methods [9] [10], are borrowed from data compression field. Others well known methods are blind deconvolution [11] and zero sheets algorithms [12]. A few of these methods require the knowledge of the Point Spread Function (PSF), others use statistical information. Others recent approaches like in [15], gives good results, but are not suitable for real time application. Our work starts from consideration found in [13], [14], which propose complete blind restoration systems. In [13], analyzed images are subdivided in sub-blocks and an edge-detection algorithm, based on DCT coefficients, is applied. PSF is computed based on the average 1-D step res...

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Adaptive penalty likelihood for reconstruction of multidimensional confocal microscopy images

Zhu

Razaz

Lee

2005

Computerized Medical Imaging and Graphics

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Satellite and aerial image deconvolution using an EM method with complex wavelets

Abstract: ABSTRACT

Cited by 6 publications

References 11 publications

Exploiting full-waveform lidar data and multiresolution wavelet analysis for vertical object detection and recognition

Exploiting full-waveform lidar data and multiresolution wavelet analysis for vertical object detection and recognition

Restoration of out-of-focus images based on circle of confusion estimate

Adaptive penalty likelihood for reconstruction of multidimensional confocal microscopy images

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