Display Brightness Based Camera Parameter Control for Selfie Capture in Darkness

When a user in a dark room captures a selfie image or video using a front camera of a device, the display brightness can impact the captured image or video. Oscillations can occur due to a feedback loop between the image captured by the camera which when displayed on the device screen causes changes in display brightness. This disclosure describes techniques to automatically estimate the impact of display illumination on the face of a subject and slow down convergence. Per the techniques, exposure and/or white balance parameters for the camera are estimated using display illumination (brightness), depth (distance of the subject), and reflectance. Machine learning techniques or a lookup table or regression on historical data can be used for estimation. Oscillations in the captured image or video due to a feedback loop are reduced or eliminated

Real-time Image Signal Processor Stats Management to Save Power and CPU Cycles

This publication describes techniques and apparatuses, implemented on a digital image capture device, directed at minimizing power consumption and central processing unit (CPU) cycles during image capture and processing events. An image sensor on the device captures a scene as a frame and generates raw image data. An on-device image signal processor (ISP) receives the raw image data and generates a statistics output (“stats output”) that includes image statistics for the frame. The stats output further includes a descriptive tag for the image statistics, saved in a header of the stats output. Software implemented on the device (e.g., a Statistics Manager) receives the stats output, parses the descriptive tag from the header, compares the descriptive tag to one or more previous descriptive tags, and determines if a change in the stats output is greater than a threshold. Upon determining that the change in the stats output is less than the threshold, the Statistics Manager determines that processing of the stats output by an Image Processing Module (e.g., 3A algorithms, other ISP software algorithms) is not necessary. Upon determining that the change in the stats output is greater than the threshold, the Statistics Manager determines processing of the stats output by the Image Processing Module is necessary. Through the use of such techniques and apparatuses, an image capture device can avoid unnecessary processing of stats outputs

A Synergistic Approach for Recovering Occlusion-Free Textured 3D Maps of Urban Facades from Heterogeneous Cartographic Data

In this paper we present a practical approach for generating an occlusion-free textured 3D map of urban facades by the synergistic use of terrestrial images, 3D point clouds and area-based information. Particularly in dense urban environments, the high presence of urban objects in front of the facades causes significant difficulties for several stages in computational building modeling. Major challenges lie on the one hand in extracting complete 3D facade quadrilateral delimitations and on the other hand in generating occlusion-free facade textures. For these reasons, we describe a straightforward approach for completing and recovering facade geometry and textures by exploiting the data complementarity of terrestrial multi-source imagery and area-based information

Color correction pipeline optimization for digital cameras

The processing pipeline of a digital camera converts the RAW image acquired by the sensor to a representation of the original scene that should be as faithful as possible. There are mainly two modules responsible for the color-rendering accuracy of a digital cam- era: the former is the illuminant estimation and correction module, and the latter is the color matrix transformation aimed to adapt the color response of the sensor to a standard color space. These two modules together form what may be called the color correction pipeline. We design and test new color correction pipelines that exploit different illuminant estimation and correction algorithms that are tuned and automatically selected on the basis of the image content. Since the illuminant estimation is an ill-posed problem, illuminant correction is not error-free. An adaptive color matrix transformation module is optimized, taking into account the behavior of the first module in order to alleviate the amplification of color errors. The proposed pipe- lines are tested on a publicly available dataset of RAW images. Experimental results show that exploiting the cross-talks between the modules of the pipeline can lead to a higher color-rendition accu- racy. © The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publica- tion, including its DOI. (DOI: 10.1117/1.JEI.22.2.023014

ViPS: Visual processing system for medical imaging

Imaging has become an indispensable tool in modern medicine. Various powerful and expensive platforms to study medical imaging applications appear in recent years. In this article, we design and propose a Visual Processing System (ViPS) that processes medical imaging applications efficiently. ViPS provides a user-friendly programming environment and high-performance architecture to perform image analysis, features extraction and object recognition for complex real-time images or videos. The data structure of image or video is described in the program memory using pattern descriptors; ViPS uses specialized 3D memory structure to handle complex images or videos and processes them on microprocessors or application specific hardware accelerators. The proposed system is highly reliable in terms of cost, performance, and power. ViPS based system is implemented and tested on a Xilinx Virtex-7 FPGA VC707 Evaluation Kit. The performance of ViPS is compared with the Intel i7 multi-core, GPU Jetson TK1 Embedded Development Kit with 192 CUDA cores based graphic systems. When compared with the Intel and GPU-based systems, the results show that ViPS performs real-time video reconstruction at 2x and 1.45x of higher frame rate, achieves 14.6x to 4.8x of speedup while executing different image processing applications and 20.3% and 12.6% of speedup for video processing algorithms respectively.Peer Reviewe

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