Matching Items (2)
Description
Self-inflicted online crises arise when a company releases materials, such as advertisements or products, that are offensive to stakeholders and consequently cause a negative reaction across online communities. This thesis examines how companies have tried to restore their image after a self-inflicted crisis arose that spoke to a lack of cultural sensitivity and understanding within the organization. Models of crisis communication were analyzed to determine that a crisis has trigger events that can be detected and prevented against. Research on diversity in the workplace and the benefits of fostering a culturally sensitive and aware workplace environment was also analyzed. Finally, image restoration strategies were examined to comprehend how companies use messaging to mitigate crises. From there, three case studies were conducted on three separate self-inflicted online crises that arose from an apparent lack of culturally sensitivity and understanding within an organization, each instance occurring within the past two years. This study then provided an analysis of the background, description, online reaction and company response to each: the PepsiCo advertisement featuring Kendall Jenner, the Gucci sweater appearing to resemble blackface and the Tarte Cosmetics Shape Tape Foundation launch. Image restoration strategies were then identified and analyzed for each case study. Metrics were determined for each case by looking at the reach of posts on social media and also by using Google Trends and Meltwater to discover the extent of media engagement during the length of each crisis. The events explored in each case study all demonstrated an oversight in the pre-crisis stage of each of the organizations, emphasizing the necessity of detection in crisis management planning as a tactic to actively identify potential threats before a triggering event can occur.
ContributorsRichards, Olivia Kathryn (Author) / Gilpin, Dawn (Thesis director) / Bovio, Sonia (Committee member) / Walter Cronkite School of Journalism & Mass Comm (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2019-05
Description
Cameras have become commonplace with wide-ranging applications of phone photography, computer vision, and medical imaging. With a growing need to reduce size and costs while maintaining image quality, the need to look past traditional style of cameras is becoming more apparent. Several non-traditional cameras have shown to be promising options for size-constraint applications, and while they may offer several advantages, they also usually are limited by image quality degradation due to optical or a need to reconstruct a captured image. In this thesis, we take a look at three of these non-traditional cameras: a pinhole camera, a diffusion-mask lensless camera, and an under-display camera (UDC).
For each of these cases, I present a feasible image restoration pipeline to correct for their particular limitations. For the pinhole camera, I present an early pipeline to allow for practical pinhole photography by reducing noise levels caused by low-light imaging, enhancing exposure levels, and sharpening the blur caused by the pinhole. For lensless cameras, we explore a neural network architecture that performs joint image reconstruction and point spread function (PSF) estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. For UDCs, we utilize a multi-stage approach to correct for low light transmission, blur, and haze. This pipeline uses a PyNET deep neural network architecture to perform a majority of the restoration, while additionally using a traditional optimization approach which is then fused in a learned manner in the second stage to improve high-frequency features. I show results from this novel fusion approach that is on-par with the state of the art.
For each of these cases, I present a feasible image restoration pipeline to correct for their particular limitations. For the pinhole camera, I present an early pipeline to allow for practical pinhole photography by reducing noise levels caused by low-light imaging, enhancing exposure levels, and sharpening the blur caused by the pinhole. For lensless cameras, we explore a neural network architecture that performs joint image reconstruction and point spread function (PSF) estimation to robustly recover images captured with multiple PSFs from different cameras. Using adversarial learning, this approach achieves improved reconstruction results that do not require explicit knowledge of the PSF at test-time and shows an added improvement in the reconstruction model’s ability to generalize to variations in the camera’s PSF. This allows lensless cameras to be utilized in a wider range of applications that require multiple cameras without the need to explicitly train a separate model for each new camera. For UDCs, we utilize a multi-stage approach to correct for low light transmission, blur, and haze. This pipeline uses a PyNET deep neural network architecture to perform a majority of the restoration, while additionally using a traditional optimization approach which is then fused in a learned manner in the second stage to improve high-frequency features. I show results from this novel fusion approach that is on-par with the state of the art.
ContributorsRego, Joshua D (Author) / Jayasuriya, Suren (Thesis advisor) / Blain Christen, Jennifer (Thesis advisor) / Turaga, Pavan (Committee member) / Arizona State University (Publisher)
Created2020