Benchmarking of mobile phone cameras

fi=vertaisarvioitu|en=peerReviewed

A Study of Colour Rendering in the In-Camera Imaging Pipeline

Consumer cameras such as digital single-lens reflex camera (DSLR) and smartphone cameras have onboard hardware that applies a series of processing steps to transform the initial captured raw sensor image to the final output image that is provided to the user. These processing steps collectively make up the in-camera image processing pipeline. This dissertation aims to study the processing steps related to colour rendering which can be categorized into two stages. The first stage is to convert an image's sensor-specific raw colour space to a device-independent perceptual colour space. The second stage is to further process the image into a display-referred colour space and includes photo-finishing routines to make the image appear visually pleasing to a human. This dissertation makes four contributions towards the study of camera colour rendering. The first contribution is the development of a software-based research platform that closely emulates the in-camera image processing pipeline hardware. This platform allows the examination of the various image states of the captured image as it is processed from the sensor response to the final display output. Our second contribution is to demonstrate the advantage of having access to intermediate image states within the in-camera pipeline that provide more accurate colourimetric consistency among multiple cameras. Our third contribution is to analyze the current colourimetric method used by consumer cameras and to propose a modification that is able to improve its colour accuracy. Our fourth contribution is to describe how to customize a camera imaging pipeline using machine vision cameras to produce high-quality perceptual images for dermatological applications. The dissertation concludes with a summary and future directions

Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices

[EN] The collection of oranges normally begins before they have reached the typical orange colour. Moreover, citrus fruits are subjected to certain degreening treatments that depend on the standard citrus colour index (CCI) at harvest. In order to facilitate the measure of this index, a free application that uses image processing techniques has been developed for Android-based mobile devices using the built-in camera of the device. The image analysis process is performed on all the images from the live input of the camera to obtain the CCI of such fruit using the open source OpenCV library. For this purpose, the RGB (red, green and blue colour coordinates) average value of a pre-selected area of the input image is calculated and then converted to HunterLab colour space to finally calculate the CCI. Several tests were carried out in the field with the fruit on the trees and under laboratory conditions with different varieties of oranges (Navel, Bonanza, Cram and Navelina) at different stages of maturity, and using different Android devices. The results were obtained for each device and condition in relation to the colour measured by a camera and compared with the performance of a panel of workers who evaluated the colour using the traditional methods. Best R-2 values obtained were 0.854 for outdoors conditions and 0.881 when measurements were done indoors.This work was partially funded by INIA and FEDER funds through research project RTA2015-00078-00-00.Cubero-García, S.; Albert Gil, FE.; Prats-Montalbán, JM.; Fernandez-Pacheco, DG.; Blasco Ivars, J.; Aleixos Borrás, MN. (2018). Application for the estimation of the standard citrus colour index (CCI) using image processing in mobile devices. Biosystems Engineering. 167:63-74. doi:10.1016/j.biosystemseng.2017.12.012S637416

Multi-contrast imaging and digital refocusing on a mobile microscope with a domed LED array

We demonstrate the design and application of an add-on device for improving the diagnostic and research capabilities of CellScope--a low-cost, smartphone-based point-of-care microscope. We replace the single LED illumination of the original CellScope with a programmable domed LED array. By leveraging recent advances in computational illumination, this new device enables simultaneous multi-contrast imaging with brightfield, darkfield, and phase imaging modes. Further, we scan through illumination angles to capture lightfield datasets, which can be used to recover 3D intensity and phase images without any hardware changes. This digital refocusing procedure can be used for either 3D imaging or software-only focus correction, reducing the need for precise mechanical focusing during field experiments. All acquisition and processing is performed on the mobile phone and controlled through a smartphone application, making the computational microscope compact and portable. Using multiple samples and different objective magnifications, we demonstrate that the performance of our device is comparable to that of a commercial microscope. This unique device platform extends the field imaging capabilities of CellScope, opening up new clinical and research possibilities

Recent Advances in Smartphone Computational Photography

Smartphone cameras present many challenges, most of which come from the need for them to be physically small. Their small size puts a fundamental limit on their ability to resolve detail and collect light, which makes low-light photography and zooming difficult. This paper presents two approaches to improve smartphone photography through software techniques. The first is handheld super-resolution which uses natural hand movement to improve the resolution smartphone images, especially when zoomed. The second approach is a system which improves low light photography in smartphones