High-dynamic-range imaging

About: High-dynamic-range imaging is a research topic. Over the lifetime, 766 publications have been published within this topic receiving 22577 citations.

A Generative Adversarial Network for Tone Mapping HDR Images

Abstract: A tone mapping operator converts High Dynamic Range (HDR) images to Low Dynamic Range (LDR) images, which can be seen on LDR displays. There has been a lot of research done in the direction of an optimal Tone Mapping Operator which maximizes Tone Mapping Quality Index (TMQI). However, since all the methods approximate Human Vision System in one or different way, none of them works for every type of images. We are proposing a novel generative adversarial network to learn a combination of these tone mapping operators. In order to get pixel level accuracy, we are using residual connections between same sized network layers. We compare this method with some of the existing tone mapping operators and observe that our method generates images with comparably high TMQI and indeed works on many different types of images. Because of the residual connections, the network can be scaled to very high dimensional images.

High Dynamic Range Imaging and Low Dynamic Range Expansion for Generating HDR Content.

Abstract: In the last few years, researchers in the field of High Dynamic Range (HDR) Imaging have focused on providing tools for expanding Low Dynamic Range (LDR) content for the generation of HDR images due to the growing popularity of HDR in applications, such as photography and rendering via Image-Based Lighting, and the imminent arrival of HDR displays to the consumer market. LDR content expansion is required due to the lack of fast and reliable consumer level HDR capture for still images and videos. Furthermore, LDR content expansion, will allow the re-use of legacy LDR stills, videos and LDR applications created, over the last century and more, to be widely available. The use of certain LDR expansion methods, those that are based on the inversion of Tone Mapping Operators (TMOs), has made it possible to create novel compression algorithms that tackle the problem of the size of HDR content storage, which remains one of the major obstacles to be overcome for the adoption of HDR. These methods are used in conjunction with traditional LDR compression methods and can evolve accordingly. The goal of this report is to provide a comprehensive overview on HDR Imaging, and an in depth review on these emerging topics.

Tone mapping using color correction function and image decomposition in high dynamic range imaging

Abstract: In this paper, we propose a tone mapping (TM) method using color correction function (CCF) and image decomposition in high dynamic range (HDR) imaging. The CCF in the proposed TM is derived from the luminance compression function with the color constraint under which the color ratios, between the three color channels of the radiance map and dynamic range compression term, are preserved and color saturation is controlled. The proposed CCF is developed to locally perform the luminance compression and color saturation control in local TM. For image decomposition, we use a bilateral filter and apply the adaptive weight to the base layer of the luminance. Computer simulations with various sets of real, low dynamic range images show the effectiveness of the proposed TM algorithm in terms of the visual quality as well the local contrast. It can be used for contrast and color enhancement in various display and acquisition devices.

Backlight simulation at reduced resolutions to determine spatial modulation of light for high dynamic range images

Abstract: Embodiments of the invention relate generally to generating images with an enhanced range of brightness levels, and more particularly, to facilitating high dynamic range imaging by adjusting pixel data and/or using predicted values of luminance, for example, at different resolutions. In at least one embodiment, a method generates an image with an enhanced range of brightness levels. The method can include accessing a model of backlight that includes data representing values of luminance for a number of first samples. The method also can include inverting the values of luminance, as well as upsampling inverted values of luminance to determine upsampled values of luminance. Further, the method can include scaling pixel data for a number of second samples by the upsampled values of luminance to control a modulator to generate an image.

Fuzzy fusion based high dynamic range imaging using adaptive histogram separation

Abstract: In this work, a high dynamic range (HDR) image generation method using a single input image is presented. The proposed approach generates over- and under-exposed images by making use of a novel adaptive histogram separation scheme. Thus, it becomes possible to eliminate ghosting effects which generally occur when several input image containing camera/object motion are utilized in HDR imaging. Additionally, it is proposed to utilize a fuzzy logic based approach at the fusion stage which takes visibility of the inputs pixels into account. Since the proposed approach is computationally light-weight, it is possible to implement it on mobile devices such as smart phones and compact cameras. Experimental results show that the proposed approach is able to provide ghost-free and improved HDR performance compared to the existing methods1.