Multi-exposure HDR capture

Summary

In photography and videography, multi-exposure HDR capture is a technique that creates extended or high dynamic range (HDR) images by taking and combining multiple exposures of the same subject matter at different exposure levels. Combining multiple images in this way results in an image with a greater dynamic range than what would be possible by taking one single image. The technique can also be used to capture video by taking and combining multiple exposures for each frame of the video. The term "HDR" is used frequently to refer to the process of creating HDR images from multiple exposures. Many smartphones have an automated HDR feature that relies on computational imaging techniques to capture and combine multiple exposures. A single image captured by a camera provides a finite range of luminosity inherent to the medium, whether it is a digital sensor or film. Outside this range, tonal information is lost and no features are visible; tones that exceed the range are "burned out" a

Official source

https://en.wikipedia.org/wiki/Multi-exposure_HDR_capture

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (3)

Related publications

Related people

Related units

Related concepts (25)

Related concepts

Related courses (7)

Related courses

Related lectures (10)

Related lectures

, ,

Sharing pictures has become a very popular practice among consumers. Most recent cameras, displays, and smartphones can capture and display images in high dynamic range and wide colour gamut, contributing to an increase of this type of content. It is a well-known fact that pictures contain information that could cause various security and privacy issues. This is in particular true because high dynamic range pictures exhibit more information when compared to conventional images resulting in an increased invasion of privacy. Lack of proper solutions to overcome privacy can hinder a wide spread adoption of high dynamic range pictures shared in social networks. For example, dark areas of a content that are otherwise difficult to view in a conventional picture become more visible. It is therefore natural that mechanisms are offered to protect privacy while sharing pictures also in high dynamic range. In this paper, we propose an architecture to share high dynamic range images captured by high-end smartphones while offering a mechanism to protect privacy. In our approach, the high dynamic range picture is tone-mapped into a lower dynamic range version which reduces its degree of invasiveness as far as privacy is concerned. Eventually, any remaining privacy sensitive areas in the picture can be further protected (blurring of faces, masking cars licence plates, etc.). The core of the proposed architecture conforms to both JPEG XT, a recently standardised format that offers backward compatibility with legacy JPEG and to JPEG Systems under development. The proposed solution allows to publicly share a protected version of the high dynamic range image tone-mapped to a standard dynamic range picture. The latter can go through transmorphing operation to further protect it. Authorised users can access the original high dynamic range picture. The architecture as described above has been implemented as an app for smartphones running Android OS. We demonstrate feasibility and usefulness of this approach and discuss its advantages when compared to conventional image sharing such as those used in social networks.

SPIE2020

, ,

Discomfort glare is a major challenge for the design of workplaces. The existing metrics for discomfort glare prediction share the limitation that they do not take gaze direction into account. In order to overcome this limitation, we developed a ‘gaze-driven’ method for discomfort glare assessment. We conducted a series of experiments under simulated office conditions and recorded the participants’ gaze using mobile eye tracking and the luminance distributions using high dynamic range imaging methods. The two methods were then integrated in order to derive ‘gaze-centred’ luminance measurements in the field of view. The existing ‘fixed-gaze’ and the newly developed ‘gaze-driven’ measurement methods are compared. Our results show that there is a significant difference between the two methods. In this paper the procedure for integrating the recorded luminance images with the recorded gaze dynamics for obtaining gaze-centred luminance data is described. This gaze-centred luminance data will be compared to the subjective assessment of glare in Part 2 of this study.

2017

In this work, an iterative learning control (ILC) is applied to a Delta robot in order to improve the tracking precision at high dynamic movement. Delta robot is a parallel manipulator designed for high-speed pick and place operations. Since the dynamic of Delta robot is highly coupled and nonlinear, the conventional controller like the proportional derivative (PD) failed to satisfy the required performances. To solve this problem a suitable controller for repetitive pick and place operations represents in the ILC has been introduced. The learning controller is combined with a PD controller to improve the tracking error through the iterations. Numerous simulations and robustness tests are carried out to demonstrate the effectiveness of the proposed scheme.

SPRINGER2019

, ,

SPIE2020