Simultaneous structure and texture image inpainting

Bertalmío, Marcelo; Vese, Luminita A.; Sapiro, Guillermo; Osher, Stanley

doi:10.1109/tip.2003.815261

Cited by 936 publications

(502 citation statements)

References 25 publications

(21 reference statements)

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“…This is especially true where extrapolation into long gaps at the beginning or the end of a time series is needed. In 10 analogous situations where IIP is used to reconstruct missing areas in images, techniques based on finding and copying similar texture structure from other patches can be further explored (Bertalmio et al, 2003). Applying a similar approach to gap-filling NEE would require a hybrid of IIP for texture rendering and process-based understanding ecosystem dynamics (Knorr and Kattge, 2005) for texture mapping.…”

Section: Discussionmentioning

confidence: 99%

A robust gap-filling method for Net Ecosystem Exchange based on Cahn–Hilliard inpainting

Rayment

2016

Preprint

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Abstract. Traditional gap-filling approaches adopt a temporally linear perspective on data; whether synthesizing data statistically within a moving window, or using complex functions based on a "best-guess" understanding of the processes driving exchange. The former approach is limited in its ability to capture non-linear trends, and the latter is limited in situations where the flux response to driving variables is poorly understood or unknown (e.g. the response of gas exchange to water table depth in wetlands). Rearranging time-averaged half-hourly net ecosystem exchange (NEE) into a 48*N matrix 10 has been used to visualize NEE as a "flux fingerprint" and suggests a different way of filling data gaps. In this paper, we introduce an image processing technique known as image inpainting to fill gaps in this two-dimensional representation of a one-dimensional data. This has the advantage that any short-term structure can be accommodated without expressly implying any particular functional response to driving environmental variables, and medium-term temporal structure (i.e. day-to-day covariance) can be incorporated into gaps in the flux signal. In this way, data gaps are filled solely using information 15 contained in robust, primary data. This new method compares favorably with the marginal distribution sampling (MDS), when tested on twelve European-Flux datasets with four types of artificial gaps. Furthermore, we show that how random structures or noise embedded in the signal affect the gap-filling performance, which can simply be improved through a denoising procedure by using a Fourier transform algorithm. The inpainting-based gap-filling approach is more effective than MDS on the de-noised data. 20

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Section: Discussionmentioning

confidence: 99%

A robust gap-filling method for Net Ecosystem Exchange based on Cahn–Hilliard inpainting

Rayment

2016

Preprint

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“…The problem of recovering lost data in images [41,42] and surfaces [43,44] is referred to as hole filling or surface filling. Intensive research is being devoted to this topic in the computer vision community.…”

Section: Discussionmentioning

confidence: 99%

Reliability measure for shape-from-focus

Pertuz

Puig

García

2013

Image and Vision Computing

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This is the author’s version of a work that was accepted for publication in Journal Image and Vision Computing . Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Journal Image and Vision Computing , 31, 10 (2013) DOI: 10.1016/j.imavis.2013.07.005Shape-from-focus (SFF) is a passive technique widely used in image processing for obtaining depth-maps. This technique is attractive since it only requires a single monocular camera with focus control, thus avoiding correspondence problems typically found in stereo, as well as more expensive capturing devices. However, one of its main drawbacks is its poor performance when the change in the focus level is difficult to detect. Most research in SFF has focused on improving the accuracy of the depth estimation. Less attention has been paid to the problem of providing quality measures in order to predict the performance of SFF without prior knowledge of the recovered scene. This paper proposes a reliability measure aimed at assessing the quality of the depth-map obtained using SFF. The proposed reliability measure (the R-measure) analyses the shape of the focus measure function and estimates the likelihood of obtaining an accurate depth estimation without any previous knowledge of the recovered scene. The proposed R-measure is then applied for determining the image regions where SFF will not perform correctly in order to discard them. Experiments with both synthetic and real scenes are presented

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“…The interferometer visibility in the reference images averaged 5% over the field of view, with the exception of a few damaged regions in the gratings. For regions in the reference visibility image with less than 3%, a mask was generated and used to guide an inpainting correction to the projection [20]. Inpainting has characteristics similar to a median filter, but when guided by a visibility mask, it offers a more targeted image correction.…”

Section: Neutron Interferometry Analysismentioning

confidence: 99%

Porosity detection in electron beam-melted Ti-6Al-4V using high-resolution neutron imaging and grating-based interferometry

Brooks

Kirka

et al. 2017

Prog Addit Manuf

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A high-resolution neutron tomography system and a grating-based interferometer are used to explore electron beam-melted titanium test objects. The high-resolution neutron tomography system (attenuation-based imaging) has a pixel size of 6.4 lm, appropriate for detecting voids near 25 lm over a (1.5 cm) 3 volume. The neutron interferometer provides dark-field (small-angle scattering) images with a pixel size of 30 lm. Moreover, the interferometer can be tuned to a scattering length, in this case, 1.97 lm, with a field-of-view of (6 cm) 3 . The combination of high-resolution imaging with grating-based interferometry provides a way for nondestructive testing of defective titanium samples. A chimney-like pore structure was discovered in the attenuation and dark-field images along one face of an electron beam-melted (EBM) Ti-6Al-4V cube. Tomographic reconstructions of the titanium samples are utilized as a source for a binary volume and for skeletonization of the pores. The dark-field volume shows features with dimensions near and smaller than the interferometer auto-correlation scattering length.

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Simultaneous structure and texture image inpainting

Cited by 936 publications

References 25 publications

A robust gap-filling method for Net Ecosystem Exchange based on Cahn–Hilliard inpainting

A robust gap-filling method for Net Ecosystem Exchange based on Cahn–Hilliard inpainting

Reliability measure for shape-from-focus

Porosity detection in electron beam-melted Ti-6Al-4V using high-resolution neutron imaging and grating-based interferometry

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