The Conference on Historic Site Archaeology Papers 1980 - Volume 15

This multi-article volume was edited by Stanley South of the South Carolina Institute of Archaeology and Anthropology. Contents: The Chairman\u27s Final Report for the Final Volume of this Series - Stanley South.....ii Population and Trade: An Economic Mathematical Model - GOGGIN AWARD - Timothy William Jones.....1 Archeological Patterning on the Frontier: the Functional Significance of the Carolina and Frontier Patterns - Kenneth E. Lewis.....50 Pattern in Pattern Recognition? - Marc G. Stevenson.....57 The Early Fur Trade Artifact Pattern - Michael R.A. Foresman.....71 Excavation of a Whiskey Still in Northeast Georgia - Patrick H. Garrow.....91 Early Nineteenth Century Blast Furnace Charcoals: Analysis and Economics - John R. White.....106 A Taxonomy for Square Cut Nails - Donna L. Benson.....123 Historical Archaeology at an Union Pacific Railroad Station in the Red Desert of Wyoming - William B. Fawcett, Jr. and Ken Erickson.....153 The Sepulveda Project: Archaeological Investigations at the El Pueblo de Los Angeles State Historic Park, Los Angeles, California - Janet Hightower.....177 Recollections of Jim Howard: A Scholarly Antagonist - Melburn D. Thurman.....190https://scholarcommons.sc.edu/archanth_historic_site_arch_conf_papers/1014/thumbnail.jp

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods

Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network

Recently, several models based on deep neural networks have achieved great success in terms of both reconstruction accuracy and computational performance for single image super-resolution. In these methods, the low resolution (LR) input image is upscaled to the high resolution (HR) space using a single filter, commonly bicubic interpolation, before reconstruction. This means that the super-resolution (SR) operation is performed in HR space. We demonstrate that this is sub-optimal and adds computational complexity. In this paper, we present the first convolutional neural network (CNN) capable of real-time SR of 1080p videos on a single K2 GPU. To achieve this, we propose a novel CNN architecture where the feature maps are extracted in the LR space. In addition, we introduce an efficient sub-pixel convolution layer which learns an array of upscaling filters to upscale the final LR feature maps into the HR output. By doing so, we effectively replace the handcrafted bicubic filter in the SR pipeline with more complex upscaling filters specifically trained for each feature map, whilst also reducing the computational complexity of the overall SR operation. We evaluate the proposed approach using images and videos from publicly available datasets and show that it performs significantly better (+0.15dB on Images and +0.39dB on Videos) and is an order of magnitude faster than previous CNN-based methods