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.

Multidimensional image enhancement from a set of unregistered and differently exposed images

Abstract: If multiple images of a scene are available instead of a single image, we can use the additional information conveyed by the set of images to generate a higher quality image. This can be done along multiple dimensions. Super-resolution algorithms use a set of shifted and rotated low resolution images to create a high resolution image. High dynamic range imaging techniques combine images with different exposure times to generate an image with a higher dynamic range. In this paper, we present a novel method to combine both techniques and construct a high resolution, high dynamic range image from a set of shifted images with varying exposure times. We first estimate the camera response function, and convert each of the input images to an exposure invariant space. Next, we estimate the motion between the input images. Finally, we reconstruct a high resolution, high dynamic range image using an interpolation from the non-uniformly sampled pixels. Applications of such an approach can be found in various domains, such as surveillance cameras, consumer digital cameras, etc.

Piecewise tone reproduction for high dynamic range imaging

Abstract: To display high dynamic range (HDR) images onto conventional displayable devices that have low dynamic range (LDR) such as monitors and printers, we propose a piecewise tone reproduction operator with chromatic adaptation. The strong point of our operator is to reproduce displayable LDR images while maintaining a perceptual match between the real world and the displayed image. The algorithm for dynamic range reduction relies on piecewise constructs and suitable tone reproduction functions that depend on the estimations of global luminance modification and local luminance adaptation. Combined with dynamic range reduction, the proposed algorithm also applies the chromatic adaptation technique of the color appearance model in order to preserve the chromatic appearance and color consistency across scene and display environments. The experimental results show that the proposed algorithm achieves good subjective results while preserving details of the image. Furthermore, the proposed algorithm has a fast, simple and practical structure for implementation.

High-dynamic-range imaging for cloud segmentation

Abstract: . Sky–cloud images obtained from ground-based sky cameras are usually captured using a fisheye lens with a wide field of view. However, the sky exhibits a large dynamic range in terms of luminance, more than a conventional camera can capture. It is thus difficult to capture the details of an entire scene with a regular camera in a single shot. In most cases, the circumsolar region is overexposed, and the regions near the horizon are underexposed. This renders cloud segmentation for such images difficult. In this paper, we propose HDRCloudSeg – an effective method for cloud segmentation using high-dynamic-range (HDR) imaging based on multi-exposure fusion. We describe the HDR image generation process and release a new database to the community for benchmarking. Our proposed approach is the first using HDR radiance maps for cloud segmentation and achieves very good results.

Method and Apparatus for Auto Exposure Value Detection for High Dynamic Range Imaging

Abstract: A method and apparatus for auto exposure value detection for High Dynamic Range (HDR) Imaging. An image can be captured as a captured image at a capture exposure. The exposure of a current iteration of the image can be changed by at least a fraction of an exposure value. A changed exposure image can be captured at the changed exposure value. A difference between an exposure data metric of the current iteration of the changed exposure image and an exposure data metric of a previous iteration of the image can be ascertained. The difference between the exposure data metric of the current iteration of the changed exposure image and the exposure data metric of the previous iteration of the image can be compared to a difference threshold. Changing the exposure value, capturing the changed exposure image, ascertaining the difference, and comparing the difference can be repeated when the difference between the exposure data metric of the current iteration of the changed exposure image and the exposure data metric of the previous iteration of the image exceeds the difference threshold.