Reflectance Transformation Imaging (RTI) System for Ancient Documentary Artefacts

This tutorial summarises our uses of reflectance transformation imaging in archaeological contexts. It introduces the UK AHRC funded project reflectance Transformation Imaging for Anciant Documentary Artefacts and demonstrates imaging methodologies

High dynamic range imaging for archaeological recording

This paper notes the adoption of digital photography as a primary recording means within archaeology, and reviews some issues and problems that this presents. Particular attention is given to the problems of recording high-contrast scenes in archaeology and High Dynamic Range imaging using multiple exposures is suggested as a means of providing an archive of high-contrast scenes that can later be tone-mapped to provide a variety of visualisations. Exposure fusion is also considered, although it is noted that this has some disadvantages. Three case studies are then presented (1) a very high contrast photograph taken from within a rock-cut tomb at Cala Morell, Menorca (2) an archaeological test pitting exercise requiring rapid acquisition of photographic records in challenging circumstances and (3) legacy material consisting of three differently exposed colour positive (slide) photographs of the same scene. In each case, HDR methods are shown to significantly aid the generation of a high quality illustrative record photograph, and it is concluded that HDR imaging could serve an effective role in archaeological photographic recording, although there remain problems of archiving and distributing HDR radiance map data

Learned Perceptual Image Enhancement

Learning a typical image enhancement pipeline involves minimization of a loss function between enhanced and reference images. While L1 and L2 losses are perhaps the most widely used functions for this purpose, they do not necessarily lead to perceptually compelling results. In this paper, we show that adding a learned no-reference image quality metric to the loss can significantly improve enhancement operators. This metric is implemented using a CNN (convolutional neural network) trained on a large-scale dataset labelled with aesthetic preferences of human raters. This loss allows us to conveniently perform back-propagation in our learning framework to simultaneously optimize for similarity to a given ground truth reference and perceptual quality. This perceptual loss is only used to train parameters of image processing operators, and does not impose any extra complexity at inference time. Our experiments demonstrate that this loss can be effective for tuning a variety of operators such as local tone mapping and dehazing

Method of obtaining intensified image from developed photographic films and plates

A method is explained of obtaining intensified images from silver images on developed photographic films and plates. The steps involve converting silver of the developed film or plate to a radioactive compound by treatment with an aqueous alkaline solution of an organo-S35 compound; placing the treated film or plate in direct contact with a receiver film which is then exposed by radiation from the activated film; and developing and fixing the resulting intensified image on the receiver film

Biocontamination and particulate detection system

A method for determining the characteristics and amount of microscopic contaminants lodged on a photographed surface is disclosed. An image enhanced full color photographic negative and print are taken of the contaminated surface. Three black and white prints are developed subsequently from red, green and blue separation filter overlays of the color negative. Both the color and three monochromatic prints are then scanned to extract in digital form a profile of any contaminant possibly existing on the surface. The resulting profiles are electronically analyzed and compared with data already stored relating to known contaminants