Missing data correction in still images and image sequences

Bornard, R.; Lecan, Emmanuelle; Laborelli, Louis; Chenot, Jean-Hugues

doi:10.1145/641083.641084

Cited by 32 publications

(53 citation statements)

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“…Several methods [17–19] are shown to be robust in correcting local illumination changes. Most of these methods adjust the pixel intensity value of the image using a nonlinear mapping function for illumination correction based on the estimated local illumination at each pixel location and combining the adjusted illumination image with the reflectance image to generate an output image.…”

Section: Methodsmentioning

confidence: 99%

Automated Diagnosis of Otitis Media: Vocabulary and Grammar

Kuruvilla

Shaikh

Hoberman

et al. 2013

International Journal of Biomedical Imaging

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We propose a novel automated algorithm for classifying diagnostic categories of otitis media: acute otitis media, otitis media with effusion, and no effusion. Acute otitis media represents a bacterial superinfection of the middle ear fluid, while otitis media with effusion represents a sterile effusion that tends to subside spontaneously. Diagnosing children with acute otitis media is difficult, often leading to overprescription of antibiotics as they are beneficial only for children with acute otitis media. This underscores the need for an accurate and automated diagnostic algorithm. To that end, we design a feature set understood by both otoscopists and engineers based on the actual visual cues used by otoscopists; we term this the otitis media vocabulary. We also design a process to combine the vocabulary terms based on the decision process used by otoscopists; we term this the otitis media grammar. The algorithm achieves 89.9% classification accuracy, outperforming both clinicians who did not receive special training and state-of-the-art classifiers.

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Section: Methodsmentioning

confidence: 99%

Automated Diagnosis of Otitis Media: Vocabulary and Grammar

Kuruvilla

Shaikh

Hoberman

et al. 2013

International Journal of Biomedical Imaging

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“…Bornald et al [10] combined the works of Efros et al [5] and Ashikhmin [7] to replace the missing data in image and image sequences. The filling order is mentioned to affect the final synthesized result.…”

Section: Introductionmentioning

confidence: 99%

A novel framework for object removal from digital photograph

Wang

Hsu

Liao

2005

IEEE International Conference on Image Processing 2005

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This work aims for a novel function for smart cameraredundant object removal from digital photograph. The proposed novel framework can fill the left lacuna region in the digital image. In previous related researches, texture synthesis and image inpainting construct the fundamentals of filling the lost image region. Texture synthesis can be used to fill the large hole of input texture, while image inpainting can be used to repair the small image gaps. In this paper, we propose an object removal framework by the sub-patch texture synthesis algorithm and weighted interpolation method with automatic repainting mechanism. In the filling process, the color distribution analysis is used to choose different methods. The exhaustive computation time is reduced by the weighted interpolation method. In order to repaint the faulty texture region intelligently, we use the color ratio gradients to detect the synthesized artifact region. The automatic artifact detection can lead repainting the faulty region without user intervention. The proposed algorithm can achieve better performance with seamless output images. The regular computation is also suitable for hardware architecture different from previous existing algorithms.

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“…We must to calculate the relative amount of the valid pixels of the original image that per pixel be included in the n LL . If the any pixel exists in the LL layer which relative the amount of the valid pixels is to satisfy the follow equation (6), and the pixel in the n LL will start being repaired in this layer. j i N 2 2 ) , ( , , the repair procedure of the pixel(i,j) will not carry out under the layer n. But the opposite pixel of the pixel (i,j) will be checked whether handle the repair procedure after the next layer by the IDWT.…”

Section: Visual Resolution Inpaintingmentioning

confidence: 99%

“…Nevertheless, the image inpainting technique has been in high demand by museums in order to repair damaged images as well as to store large amounts of image data. In order to effectively retain the image data, various researchers have continually proposed various methods of image inpainting [1][2][3][4][5][6][7][8][9]. These image inpainting methods can be divided into two forms of analysis, which can be viewed from two different perspectives: texture analysis and color analysis.…”

Section: Introductionmentioning

confidence: 99%

“…In the texture analysis, the image inpainting technique considers spatial texture directly up to the related position used [1][2][3][4]. Conversely, in the color analysis, the color components of the original image are first converted into various domains through different color system transformations, and then depending on the diverse color composition trend analyses, the color components of damaged regions are repaired separately [5][6][7][8][9]. However, the above mentioned methods are unable to combine their respective advantages in the area of image inpainting in different analysis domains.…”

Section: Introductionmentioning

confidence: 99%

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The Highly Lose Image Inpainting Method Based on Human Vision

Hsieh

Lai

Yang

et al.

First International Conference on Innovative Computing, Information and Control - Volume I (ICICIC'06)

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Currently, noise interference and data loss are two major problems that affect the processing results of image data transmission and storage. In order to restore damaged image data effectively, we propose a novel image inpainting technique based on wavelet transformation. The primary feature of our proposed technique is to separate the given image into two principal components which encompass image texture and color respectively. Then, according to the distinctive qualities of the given image, various image inpainting methods are adopted to perform image repair. By taking advantage of the separation of an image into its individual frequency components, we use the multi-resolution characteristics of wavelet transform, from the lowest spatial-frequency layer to the higher one, to analyze the image from global-area to local-area progressively. In order to substantiate the effectiveness of our proposed image inpainting method, we employed various images subject to high noise interference and/or extensive data loss or distortion. The experimental results were perfect, even if the distortion portions of the repaired images were higher than 90%.

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Missing data correction in still images and image sequences

Cited by 32 publications

References 0 publications

Automated Diagnosis of Otitis Media: Vocabulary and Grammar

Automated Diagnosis of Otitis Media: Vocabulary and Grammar

A novel framework for object removal from digital photograph

The Highly Lose Image Inpainting Method Based on Human Vision

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