New Farmers of America Association

Two young men standing in the water.https://digital.library.ncat.edu/photos/1091/thumbnail.jp

New Farmers of America Association

Young man in suit.https://digital.library.ncat.edu/photos/1589/thumbnail.jp

New Farmers of America Association

Members of the New Farmers of America, Shawtown Chapter, gathered for a meal.https://digital.library.ncat.edu/photos/1139/thumbnail.jp

New Farmers of America Association

A group of young men displaying carpentry projects. W. T. Johnson is standing on the far right.https://digital.library.ncat.edu/photos/1128/thumbnail.jp

New Farmers of America Association

Boy and woman walking together.https://digital.library.ncat.edu/photos/1557/thumbnail.jp

New Farmers of America Association

A man and young boy on a Farmall tractor pulling a float down the street.https://digital.library.ncat.edu/photos/1004/thumbnail.jp

New Farmers of America Association

A group of men and a woman standing in front of the Dr. H. O. Sargent Award exhibit. Standing fifth from the left is Dr. A. P. Bell, former distinguished professor and chairperson of agriculture at North Carolina Agriculture and Technical State University. On the far left is MeeCee Baker, a member of the National Vocational Agriculture Teachers Association.https://digital.library.ncat.edu/photos/1136/thumbnail.jp

New Farmers of America Association

Wooden building surrounded by trees.https://digital.library.ncat.edu/photos/1460/thumbnail.jp

Green Aggie: An Aggie’s Guide to Green Products and Sustainable Living Magazine

Inaugural edition of Green Aggie: An Aggie’s Guide to Green Products & Sustainable Living magazine, a digital publication inspired by the Liberal Studies 220: Race, Class, and Environmental Quality course. Fall 2023 LIBS 220 StudentsAdams, Chase; Adams, Sadiyah; Austin, Amari; Boutté, Ahmara; Caldwell, Ger-Cari; Campbell, Keira; Chin, Jalen; Cole, Avarion; Cook, Zalyn; Davis, Albeion; Davis, Leeah; Dowd, Chance; Ford, Drezuan; Gomes, Simplicio; Guerra, Jon-Paul; Harshaw, Kayla; Horne, Jordyn; Humes, Troi; Lovieno, Ayanna Johnson, Maya; Lagrone, Lawrence; Logan, Michael; McClarin, Trinity; Monge, David; Nicholson, Camron; Pough, Latavia; Rice, Brandon; Riley, Amari; Sanders, Jayden; Sharpe, Korion; Thompson, Daneesha; Transou, Jayla; Wescott, Jada; Whitten, Kia; Woodburn, Naiyahttps://digital.library.ncat.edu/greenaggiemag/1000/thumbnail.jp

Facial Image Deblurring & Recognition using GFP-GAN

Technology captures facial images of people in a variety of ways, many of which produce blurred images or those of unrecognizable quality. It is important to develop methods to enhance and deblur those images. This research covers the use of the Multi-Frame Labeled Faces Database (MFLFD), the algorithm/method that aims to deblur facial images in the database, and the results and implications of using the method. The database consists of two datasets TRAIN and TEST which contain over 12000 blurred facial images captured from YouTube. This research will discuss what was used to resize the images to 250x250 pixels. The algorithm uses the GFP-GAN framework to deblur and recognize the facial images provided by the database. The GFP-GAN framework uses a degradation removal module and a pre-trained face GAN. By use of the GFP-GAN framework the goal of the algorithm is to remove the features that degrade and blur the image and produce one that is closest to ground-truth. The research will conclude with the challenges and next-steps regarding the algorithm