Image Restoration

David Letterman, host of The Late Show, told a joke about Sarah Palin’s daughter during one of his nightly monologues. During the ensuing controversy, Letterman offered two extended statements about the joke. Approaching Letterman’s responses from the perspective of Benoit’s image repair typology reveals three primary strategies: denial, mortification, and corrective action. While Letterman’s first attempt to resolve the controversy failed, his second attempt was largely successful. Implications for public relations image repair strategies are offered.

Rainy image restoration is considered as one of the most important image restorations aspects to improve the outdoor vision. Many fields have used this kind of restorations such as driving assistant, environment monitoring, animals monitoring, computer vision, face recognition, object recognition and personal photos. Image restoration simply means how to remove the noise from the images. Most of the images have some noises from the environment. Moreover, image quality assessment plays an important role in the valuation of image enhancement algorithms. In this research, we will use a total variation to remove rain streaks from a single image. It shows a good performance compared to other methods, using some measurements MSE, PSNR, and VIF for an image with references and BRISQUE for an image without references.

Image blur and noise are difficult to avoid in many situations and can often ruin a photograph. We present a novel image deconvolution algorithm that deblurs and denoises an image given a known shift-invariant blur kernel. Our algorithm uses local color statistics derived from the image as a constraint in a unified framework that can be used for deblurring, denoising, and upsampling. A pixel's color is required to be a linear combination of the two most prevalent colors within a neighborhood of the pixel. This two-color prior has two major benefits: it is tuned to the content of the particular image and it serves to decouple edge sharpness from edge strength. Our unified algorithm for deblurring and denoising out-performs previous methods that are specialized for these individual applications. We demonstrate this with both qualitative results and extensive quantitative comparisons that show that we can out-perform previous methods by approximately 1 to 3 DB.

In standard digital color imaging, each pixel position acquires data for only one color plane and the remaining two color planes must be inferred through a process known as demosaicking. Furthermore, the image is susceptible to blurring artifacts due to a moving camera or fast moving subject. In this work we develop a robust framework to demosaick the color filter array (CFA) image while reducing the blur corrupting the image. We begin by defining a color motion blur model that describes the motion blur artifacts affecting color images. We then integrate the motion blur model in the demosaicking algorithm to obtain a computationally efficient framework for deblurring while demosaicking.

We propose the audio inpainting framework that recovers portions of audio data distorted due to impairments such as impulsive noise, clipping, and packet loss. In this framework, the distorted data are treated as missing and their location is assumed to be known. The signal is decomposed into overlapping time-domain frames and the restoration problem is then formulated as an inverse problem per audio frame. Sparse representation modeling is employed per frame, and each inverse problem is solved using the Orthogonal Matching Pursuit algorithm together with a discrete cosine or a Gabor dictionary. The Signal-to-Noise Ratio performance of this algorithm is shown to be comparable or better than state-of-the-art methods when blocks of samples of variable durations are missing. We also demonstrate that the size of the block of missing samples, rather than the overall number of missing samples, is a crucial parameter for high quality signal restoration. We further introduce a constrained Matching Pursuit approach for the special case of audio declipping that exploits the sign pattern of clipped audio samples and their maximal absolute value, as well as allowing the user to specify the maximum amplitude of the signal. This approach is shown to outperform state-of-the-art and commercially available methods for audio declipping in terms of Signal-to-Noise Ratio.

Checking railway status is critical to guarantee high operating safety, proper maintenance schedule, and low maintenance and operating costs. This operation consists of the analysis of the rail profile and level as well as overall geometry and ondulation. Traditional detection systems are based on mechanical devices in contact with the track. Innovative approaches are based on laser scanning and image analysis. This paper presents an efficient composite technique for track profile extraction with real-time image processing. High throughput is obtained by algorithmic prefiltering to restrict the image area containing the track profile, while high accuracy is achieved by neural reconstruction of the profile itself.

Image registration is used in many fields for mapping one image to another. In magnetic resonance imaging (MRI) ap-plications, one of the main uses is for correction of motion-induced artifacts so that subsequent image analysis would be more reliable. This paper gives an ...

In this work, we present a method for approximating constrained maximum entropy (ME) reconstructions of SPECT data with modifications to a block-iterative maximum a posteriori (MAP) algorithm. Maximum likelihood (ML)-based reconstruction algorithms require some form of noise smoothing. Constrained ME provides a more formal method of noise smoothing without requiring the user to select parameters. In the context of SPECT, constrained ME seeks the minimum-information image estimate among those whose projections are a given distance from the noisy measured data, with that distance determined by the magnitude of the Poisson noise. Images that meet the distance criterion are referred to as feasible images. We find that modeling of all principal degrading factors (attenuation, detector response, and scatter) in the reconstruction is critical because feasibility is not meaningful unless the projection model is as accurate as possible. Because the constrained ME solution is the same as a MAP solution for a particular value of the MAP weighting parameter, beta, the constrained ME solution can be found with a MAP algorithm if the correct value of beta is found. We show that the RBI-MAP algorithm, if used with a dynamic scheme for estimating beta, can approximate constrained ME solutions in 20 or fewer iterations. We compare results for various methods of achieving feasible images on a simulation of Tl-201 cardiac SPECT data. Results show that the RBI-MAP ME approximation provides images and quantitative estimates close to those from a slower algorithm that gives the true ME solution. Also, we find that the ME results have higher spatial resolution and greater high-frequency noise content than a feasibility-based stopping rule, feasibility-based low-pass filtering, and a quadratic Gibbs prior with beta selected according to the feasibility criterion. We conclude that fast ME approximation is possible using either RBI-MAP with the dynamic procedure or a feasibility-based stopping rule, and that such reconstructions may be particularly useful in applications where resolution is critical.

In this paper a two-phase filter for removing "salt and pepper" noise is proposed. In the first phase, an adaptive median filter is used to identify the set of the noisy pixels; in the second phase, these pixels are restored according to a regularization method, which contains a data-fidelity term reflecting the impulse noise characteristics. The algorithm, which exhibits good performance both in denoising and in restoration, can be easily and effectively parallelized to exploit the full power of multi-core CPUs and GPGPUs; the proposed implementation based on the FastFlow library achieves both close-to-ideal speedup and very good wall-clock execution figures.

Morphological associative memories (MAMs) are a special type of associative memory which exhibit optimal absolute storage capacity and one-step convergence. This associative model substitutes the additions and multiplications used in the classical models by additions/subtractions and maximums/minimums depending on the proposed model. MAMs have been applied to different pattern recognition problems including face localization and gray scale image restoration. Despite of his power, it has not been applied in problems that involve true-color patterns. In this paper we show how a Morphological Auto-associative Memory (MAAM) can be applied to restore true-color patterns. We present a study of the behavior of this associative model with a benchmark of 14400 images altered by different type of noises.

Abstract—Recently, digital image inpainting has attracted strong research interest because of its extensive applications in real life. The terminology ”inpainting ” refers to automatic restoration of image defects such as scratches or blotches as well as removal of unwanted objects as, for instance, subtitles, logos, etc, such that it is undetectable by viewers without the reference to the original image. Many works on this subject have been pub-lished in recent years. This paper introduces a novel unsupervised image completion framework using a modified exemplar-based method in conjunction with a pyramidal representation of an image. A top-down iterative completion is performed gradually with multi-resolution patches and a window-based priority. The proposed approach is verified on different natural images. Also, a comparison with some existing methods coming from literature is carried out and the results show improvement in favor of our approach. Keywords—inpainting, image complet...

We give and prove two new and fundamental properties of total-variationminimizing function regularization (TV regularization): edge locations of function features tend to be preserved, and under certain conditions are preserved exactly; intensity change experienced by individual features is inversely proportional to the scale of each feature. We give and prove exact analytic solutions to the TV regularization problem for simple but important cases. These can also be used to better understand the effects of TV regularization for more general cases. Our results explain why and how TV-minimizing image restoration can remove noise while leaving relatively intact larger-scaled image features, and thus why TV image restoration is especially effective in restoring images with larger-scaled features. Although TV regularization is a global problem, our results show that the effects of TV regularization on individual image features are often quite local. Our results give us a better understanding of what types of images and what types of image degradation are most effectively improved by TV-minimizing image restoration schemes, and they potentially lead to more intelligently designed TV-minimizing restoration schemes.

The Paper describes estimation and control strategies for models with bounded data uncertainties. We shall refer to them as BDU estimation and BDU control methods, for brevity. They are based on constrained game-type formulations that allow the designer to explicitly incorporate into the Problem Statement a priori information about bounds on the sizes of the uncertainties. In this way, the effect of uncertainties is not unnecessarily overemphasized beyond what is implied by the a priori bounds; consequently, overly conservative designs, as well as overly sensitive designs, are avoided. A feature of these new formulations is that geometric insights and recursive techniques, which are widely known and appreciated for classical quadratic-tost designs, tan also be pursued in this new framework. Also, algorithms for computing the optimal solutions with the same computational effort as Standard least-squares solutions exist, thus making the new formulations attractive for practical use. Moreover, the framework is broad enough to encompass applications across several disciplines, not just estimation and control. Examples will be given of a quadratic control design, an So0 control design, a total

The application of science and engineering to the analysis of priceless paintings and statues dates back several centuries. However, only over the past few decades have analytical methods developed in the physical sciences been able to glean information, from the past and contribute to the analysis, conservation, and dissemination of works of art. By specifically focusing on the visual arts (e.g., on the binomial image processing and paintings), it is the purpose of this column to: 1) sketch the main applications that could benefit from the introduction of ad hoc image processing tools, 2) outline the state-of-the-art, 3) describe the main peculiarities of this field, and 4) discuss the main obstacles impeding a more fruitful cooperation between the image processing community and art historians, restorers, and artists in general.

Single image haze removal has been a challengingproblem due to its ill-posed nature. In this paper, we propose a simple but powerful color attenuation prior for haze removal from a single input hazy image. By creating a linear model for modeling the scene depth of the hazy image under this novel prior and learning the parameters of the model with a supervised learning method, the depth information can be well recovered. With the depth map of the hazy image, we can easily estimate the transmission and restore the scene radiance via the atmospheric scattering model, and thus effectively remove the haze from a single image. Experimental results show that the proposed approach outperforms state-of-the-art haze removal algorithms in terms of both efficiency and the dehazing effect.

We propose an extension of the classical morphological filtering based on openings by line segment structuring elements. It consists in filtering a 3D+time image with the opening by all the possible rotations of a segment in the 4D space, given an initial segment and a rotation angle. The method has been applied to remove noise from confocal laser scanning microscopy images of live zebrafish embryos engineered to fluorescently label all their cell membranes.

Recently, there has been a great deal of work developing super-resolution algorithms for combining a set of lowquality images to produce a set of higher quality images. Either explicitly or implicitly, such algorithms must perform the joint task of registering and fusing the low-quality image data. While many such algorithms have been proposed, very little work has addressed the performance bounds for such problems. In this paper, we analyze the performance limits from statistical first principles using Cramér-Rao inequalities. Such analysis offers insight into the fundamental super-resolution performance bottlenecks as they relate to the subproblems of image registration, reconstruction, and image restoration.

A new impulse noise reduction method for colour images, called histogram-based fuzzy colour filter (HFC), is presented in this paper. The HFC filter is particularly effective for reducing high-impulse noise in digital images while preserving edge sharpness. Colour images that are corrupted with noise are generally filtered by applying a greyscale algorithm on each colour component separately. This approach causes artefacts especially on edge or texture pixels. Vector-based filtering methods were successfully introduced to overcome this problem. In this paper, we discuss an alternative technique so that no artefacts are introduced. The main difference between the new proposed method and the classical vector-based methods is the usage of colour component differences for the detection of impulse noise and the preservation of the colour component differences. The construction of the HFC filter involves three steps: (1) the estimation of the original histogram of the colour component differences, (2) the construction of suitable fuzzy sets for representing the linguistic values of these differences and (3) the construction of fuzzy rules that determine the output. Extensive simulation results show that the proposed filter outperforms many well-known filters (including vector-based approaches).

This article deals with the problem of restoring and motion segmenting noisy image sequences with a static background. Usually, motion segmentation and image restoration are considered separately in image sequence restoration. Moreover, motion segmentation is often noise sensitive. In this article, the motion segmentation and the image restoration parts are performed in a coupled way, allowing the motion segmentation part to positively influence the restoration part and vice-versa. This is the key of our approach that allows to deal simultaneously with the problem of restoration and motion segmentation. To this end, we propose a theoretically justified optimization problem that permits to take into account both requirements. The model is theoretically justified. Existence and unicity are proved in the space of bounded variations. A suitable numerical scheme based on half quadratic minimization is then proposed and its convergence and stability demonstrated. Experimental results obtained on noisy synthetic data and real images will illustrate the capabilities of this original and promising approach.

A self-tuning image restoration filter based on a simplified version of the Jaffe-McGlamery underwater image formation model is presented. Optimal values of the filter parameters are estimated automatically for each individual image by optimizing a quality criterion based on a global contrast measure. The simplified model is ideally suitable for diffuse-light imaging with limited backscatter, but qualitative tests show good performance in a variety of imaging conditions. In addition, quantitative tests with a large number of frames from six real mission videos indicate a substantial performance improvement when restoration is used as a preprocessor for a classifier detecting man-made objects in unconstrained subsea videos.

We propose an image restoration approach based on fusing visually similar image blocks located in a single or in multi- ple image frames of the same scene. The proposed approach copes with misalignment between frames, as well as with the presence of outliers represented by moving objects in the scene. The main application targeted by the proposed ap- proach is multi-frame image stabilization, which reduces the effect of unwanted camera motion during the image integra- tion time by fusing multiple short exposed image frames of the scene. Due to their short exposure, the individual frames are noisy, but they are less corrupted by motion blur than it would be a single long exposed frame. The proposed ap- proach is demonstrated through a series of experiments and comparisons. The results reveal the ability of the proposed method to improve the image quality by reducing noise and simulating longer exposure times.

Demosaicking is an estimation process to determine missing color values when a single-sensor digital camera is used for color image capture. In this paper, we propose a number of new methods based on the application of Taylor series and cubic spline interpolation for color filter array demosaicking. To avoid the blurring of an edge, interpolants are first estimated in four opposite directions so that no interpolation is carried out across an edge. A weighted median filter, whose filter coefficients are determined by a classifier based on an edge orientation map, is then used to produce an output from the four interpolants to preserve edges. Using the proposed methods, the original color can be faithfully reproduced with minimal amount of color artifacts even at edges.

Citation: Moody, M. (2011). The Nappy-Headed Ho: A Fantasy Theme Analysis Of The Don Imus Scandal From A Black Feminist Approach. Conference Papers -- International Communication Association, 1-24.

Abstract
This essay applies symbolic convergence theory (SCT) with its critical method, fantasy theme
analysis (FTA), to explore Don Imus’ use of fantasy themes to depict members of Rutgers
predominantly black, female basketball team as “nappy-headed hos.” This analysis concludes
that Imus was extremely successful in getting his two co-hosts to help him develop a fantasy
plotline. While the shock jock started the dialogue, his ideas caught fire, and all three men
added important elements that served to frame black women negatively. They placed great
emphasis on character development, while not referring to game highlights or individual skills
at all. Instead, they focused on superficial elements such as the basketball players looks,
describing them as masculine, nappy-headed and tattooed. Because the members of the Rutgers
basketball team did not fit their description of an ideal woman, Imus and his co-hosts resorted to
stereotypes deeply entrenched in cultural narratives of black women. Worth noting is while
highly negative, Imus’ comments brought to light other instances where people condone similar
misogynistic messages in comedy and rap music and fostered solutions to reduce both types.
Scholars must encourage essays such as this one to keep the discussion of free speech, race and
gender on the public’s agenda.

In 2001, Beef Products Incorporated (BPI) began marketing Lean Finely Textured Beef (LFTB) – cuts of beef from ribeye, sirloin, and roast trimmings treated with ammonia hydroxide to destroy possible E.coli and salmonella pathogens – a USDA-approved process since 1993. In 2002, BPI issued its first of many LFTB recalls due to E.coli, salmonella, and strong ammonia odor. The LFTB bandwagon jump-off got worse after that as school systems, grocery chains, and fast food companies canceled orders for the product. After negative media coverage about LFTB, BPI was forced to close three plant and lay off 750 employees. There is a slow recovery presently and lawsuits against media personalities and outlets. In essence, with recalls of BPI’s LFTB and the lack of labeling to give consumers a choice to consume LFTB, or not, BPI should have seen these crises coming. All the writings were on the BPI walls.
Keywords: Pink Slime, BPI, LFTB, Ground Beef, Grocery Chains, Consumer Outrage, Food Safety, Food Labeling, USDA, FDA, ABC, Chef Jamie Oliver, Image Restoration Theory, Reputation Recovery Model, Beef Recalls

This article focuses on corroborating the impressionist claim that contemporary global society is living in the age of apology. I highlight several processes that facilitate the emergence and extension of the practice of apologizing and argue that Israel, as a player in this apology game, has been fertile soil for the growing global practice. Examination of apology discourse in Israel between 1948 and 2004 indicates that the practice of apology was significantly integrated into the local public arena in 1997 following the Labor Party's apology toward the Mizrahim. The importing of the global practice into the local arena led to an expansion of the phenomenon in other fields of discourse. The adoption of the practice has turned the public apology into a common rhetorical genre in Israel, utilized by public figures and organizations as a means of image restoration and as a legitimate tool for managing social relationships. Lastly, in agreement with the rhetoricist position for language and globalization, the age of apology can be understood as a type of discourse of globalization that widens the repertoire of rhetorical resources available for contemporary public figures.

Image processing is one the hot research topic for the researchers. The image obtained from the different
sensors or satellites are degraded and are not suitable for human perception or further process. The images are
degraded by many parameters like noises in the environment or blurring of the image during image acquisition or
during processing of the image. In order to improve the quality of the image so that the required objects can be easily
accessible from the sensed images, the image restoration technique is used in the image processing. It improves the
objectivity of the image and removes the noise and blurry content in the image. In this paper we are considering four
most popular image restoration techniques like Wiener Filter, Lucy-Richardson Method, Blind De-Convolution and
regularized filter. The performances of these techniques are evaluated and compared. Different performance
parameters are considered to check the efficiency of above mentioned techniques.

We develop a low-complexity overcomplete wavelet domain method for denoising digital images corrupted with nonstationary white additive Gaussian noise. The noise level for each pixel is estimated from a local window around that pixel. We use a shrinkage function that adapts itself to the noise level and to the spatially changing statistics of the image. Experiments show that this noise model has good results for different non-stationary noise sources. Finally, we extend our method for denoising images corrupted with signal-dependent noise.

In this paper a bias correction algorithm for magnetic resonance imaging (MRI) is presented. The magnetic resonance (MR) images affected by this artifact, also called RF-inhomogeneity, exhibit irregular spatial brightness variations caused by magnetic field inhomogeneity. Here we present an original algorithm based on E2D - HUM, already proposed by some of the authors, where a modified Gabor filter is introduced in the elaboration chain to provide directional capabilities to suppress the artifact. The process of restoration doesn't care about the structure of the image and it has been applied to MR images of different parts of body like knee, abdomen, pelvis and brain. A comparison with other algorithms able to remove bias has been made.

Formula One is the most technologically advanced and financially lucrative form of motorsport in the world with global audiences in the billions over the course of a season. A highly unusual and potentially deadly set of events took place during the Friday practice for the sixth annual United States Grand Prix at the Indianapolis Motor Speedway. Controversy and blame soon followed. Using Benoit's public relations and image restoration strategy framework, this paper examines the different strategies employed during the crisis by the Formula One teams, Michelin, the Federation Internationale De L'Automobile, Formula One Management, and the Indianapolis Motor Speedway. An examination of the crisis situation at the United States Grand Prix allows this study to expand Benoit's initial framework into sport as well as examining the implications for public relations and image repair strategies.

Image restoration has major application areas of image processing technique is improving the quality of recorded images. No imaging system gives images of perfect quality because of degradation caused by various reasons. Image restoration technique deals with those images that have been recorded in the presence of one or more source of degradation. The prime focus of this paper is related to the restoration performed by different non-linear filters and there drawbacks which can to removed up to an extent using discrete wavelet transform with a chosen threshold. In this paper, some pseudo-inverse techniques and wiener filter technique have been used along with restoration using discrete wavelet transform (DWT). A comparative analysis shows that the DWT approach is superior to the pseudo-inverse and wiener technique.

Located in the remote, impenetrable and incompletely mapped rainforest of the Mosquito Coast, the legendary "Ciudad Blanca" of Honduras was never conquered by the Spanish. With time, it was slowly forgotten. JERS-1 and ERS-2 SAR images are used to identify and to locate the lost city, a task made difficult due to the thick vegetation cover. Among the techniques used to detect artificial targets under forest cover, new Bayesian speckle filters, particularly suited for the restoration of a strongly, mixed textured scene, are used to enhance the SAR images. In addition, radargrammetric techniques are used to produce a Digital Elevation Model (DEM) of the study area; and to fuse ERS and JERS information to allow easier visual identification of the remnants of "Ciudad Blanca" by visual photo-interpretation. Using the processed images, geocoded UTM spatio-maps of the region are also produced.

A critical issue in image restoration is the problem of Gaussian noise removal while keeping the integrity of relevant image information. Clinical magnetic resonance imaging (MRI) data is normally corrupted by Rician noise from the measurement process which reduces the accuracy and reliability of any automatic analysis. The quality of ultrasound (US) imaging is degraded by the presence of signal dependant noise known as speckle. It generally tends to reduce the resolution and contrast, thereby, to degrade the diagnostic accuracy of this modality. For this reasons, denoising methods are often applied to increase the: Signal-to-Noise Ratio (SNR) and improve image quality. This paper proposes a statistical filter, which is a modified version of Hybrid Median filter for noise reduction, which computes the median of the diagonal elements and the mean of the diagonal, horizontal and vertical elements in a moving window and finally the median value of the two values will be the new pixel value. The results show that our proposed method outperforms the classical implementation of the Mean, Median and Hybrid Median filter in terms of denoising quality. Comparison with well established methods, such as Total Variation, Wavelet and Wiener filters show that the proposed filter produces better denoising results, preserving the main structures and details.

The Deepwater Horizon oil spill into the Gulf of Mexico in 2010 posed an immense threat to BP´s organisational image and reputation. BP subsequently engaged in crisis communications in attempts to restore a positive image. This study uses Benoit´s (1997) image restoration discourse typology as a template for message content, identified through content analysis.

The restoration of noisy and blurred scalar images has been widely studied, and many algorithms based on variational or stochastic formulations have tried to solve this ill-posed ]. However, only few methods exist for multichannel/color images ([7, 29, 16, 36]). Here, we propose a new vector image restoration PDE which removes the noise and enhances blurred vector contours, thanks to a vector generalisation of scalar¨-function diffusions and shock filters. A local and geometric approach is proposed, which uses pertinent vector informations. Finally, we extend this equation to constrained norm evolutions, in order to restore direction fields and chromaticity noise on color images.