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.

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.

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.

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...

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 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.

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.

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.

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.