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.

Noise removal and real-time detail enhancement of high-dynamic-range infrared images with time consistency

Abstract: This paper presents a noise removal and image detail enhancement method that accounts for the limitations on human's perception to effectively visualize high-dynamic-range (HDR) infrared (IR) images. In order to represent real world scenes, IR images use to be represented by a HDR that generally exceeds the working range of common display devices (8 bits). Therefore, an effective HDR mapping without losing the perceptibility of details is needed. To do so, we introduce the use of two guided filters (GF) to generate an accurate base and detail image component. A plausibility mask is also generated from the combination of the linear coefficients that result from each GF; an indicator of the spatial detail that enables to identify those regions that are prominent to present noise in the detail image component. Finally, we filter the working range of the HDR along time to avoid global brightness fluctuations in the final 8 bit data representation, which results from combining both detail and base image components using a local adaptive gamma correction (LAGC). The last has been designed according to the human vision characteristics. The experimental evaluation shows that the proposed approach significantly enhances image details in addition to improving the contrast of the entire image. Finally, the high performance of the proposed approach makes it suitable for real word applications.

Image Fusion-Based Tone Mapping Using Gaussian Mixture Model Clustering

Abstract: Tone mapping (TM) algorithms convert high dynamic range (HDR) images into low dynamic range (LDR) images to represent on conventional display devices. Most TM methods compress the dynamic range of input HDR images by using a global transformation function (TF), and then improve local detail by applying contrast enhancement techniques. However, these approaches often fail to restore local detail lost in the dynamic range compression. To solve this problem, we propose a novel image fusion-based TM method. We use Gaussian mixture model clustering algorithm to estimate the dark and bright distributions in the luminance histogram of the input HDR image. Then, we generate two LDR images using two locally-adaptive TFs obtained by the components of each distribution. Finally, the output image is produced by the image fusion technique employing a brightness weight and a local contrast weight. The experimental results show that the proposed algorithm achieves high performance compared to state-of-the-art methods in terms of detail preservation and brightness adjustment.

Generate an artifact-free High Dynamic Range imaging

Abstract: The contrast in real world scenes is often beyond what consumer cameras can capture For these situations, High Dynamic Range (HDR) images can be generated by taking multiple exposures of the same scene When fusing information from different images, however, the slightest change in the scene can generate artifacts which dramatically limit the potential of this solution We present a technique capable of dealing with a large amount of movement in the scene: we find, in all the available exposures, patches consistent with a reference image previously selected from the stack We generate the HDR image by averaging the radiance estimates of all such regions and we compensate for camera calibration errors by removing potential seams We show that our method works even in cases when many moving objects cover large regions of the scene

Multi-illuminant color constancy for HDR images through exposure segmentation

Abstract: A standard practice in high dynamic range imaging is to compose an image through exposure bracketing which captures a series of exposures of the same scene and then combine them together, followed by dynamic rang compression and some color processing steps. Scenes lit by multiple illuminants such as a room with an artificial light source when the sun is shining through the window is an often encountered scenario which offers opportunity for the high dynamic range feature of an image pipeline to show its advantages. Traditional color constancy algorithms estimate a global white point of the scene and then apply color correction based on this estimate, which could exaggerate the difference between the illuminants, making part of the image better and part of the image worse, or compromise the color of the whole scene. In this paper, we propose a method for the color constancy of high dynamic range scenes with multiple illuminants utilizing the inherent difference in their luminance levels to assist the segmentation of the image into differently illuminated portions and apply their corresponding color constancy parameters. Experimental results using two exposures show superior performance of the proposed algorithm compared to traditional algorithms applying global corrections only.