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.

On Geometric and Photometric Registration of Images

Abstract: Finding geometric and photometric relation among images is crucial in many computer vision tasks such as panoramic imaging, high dynamic range imaging, stereo imaging, and change detection. Most photometric registration algorithms require accurate geometric registration of images. On the other hand, geometric registration may fail when images are not aligned photometrically. There are two contributions of this paper: (i) A contrast invariant feature detection algorithm is proposed. This would allow geometric registration of images without photometric registration, (ii) A photometric registration algorithm that can handle scene occlusions is presented.

Robust artifact-free high dynamic range imaging of dynamic scenes

Abstract: The irradiance range of the real-world scene is often beyond the capability of digital cameras. Therefore, High Dynamic Range (HDR) images can be generated by fusing images with different exposure of the same scene. However, moving objects pose the most severe problem in the HDR imaging, leading to the annoying ghost artifacts in the fused image. In this paper, we present a novel HDR technique to address the moving objects problem. Since the input low dynamic range (LDR) images captured by a camera act as static linear related backgrounds with moving objects during each individual exposures, we formulate the detection of foreground moving objects as a rank minimization problem. Meanwhile, in order to eliminate the image blurring caused by background slightly change of LDR images, we further rectify the background by employing the irradiances alignment. Experiments on image sequences show that the proposed algorithm performs significant gains in synthesized HDR image quality compare to state-of-the-art methods.

Improved elastic registration for removing ghost artifacts in high dynamic imaging

Abstract: In this paper, we present a ghost artifacts removing method for obtaining artifact-free high dynamic range (HDR) images in the presence of camera movement. The existing HDR methods work on condition that there is no camera movement when acquiring multiple low dynamic range (LDR) images. For overcoming such unrealistic restriction, we register the multiple LDR images by using a novel target frame finding method. Ghost artifacts are removed in the resulting HDR images since the registration process of LDR images compensates undesired camera motion. Since the proposed method can embed the self compensating function into an imaging device, Ghost-free HDR imaging application can be extended to most consumer cameras, such as mobile phone cameras, point-and-shoot cameras, and digital single lens reflected (DSLR) cameras without using additional stabilizing equipments.

Comparison of Deghosting Algorithms for Multi-exposure High Dynamic Range Imaging

Abstract: High dynamic range (HDR) images can be generated by capturing a sequence of low dynamic range (LDR) images of the same scene with different exposures and then merging those images to create an HDR image. During capturing of LDR images, any changes in the scene or slightest camera movement results in ghost artifacts in the resultant HDR image. Over the past few years many algorithms have been proposed to produce ghost free HDR images of dynamic scenes. In this study we performed subjective psychophysical experiments to evaluate four algorithms for removing ghost artifacts in the final HDR image. To our best knowledge, no evaluation of deghosting algorithms for HDR imaging has been published. Thus, the aim of this paper is not only to evaluate different ghost removal algorithms but also to introduce a methodology to evaluate such algorithms and to present some of the challenges that exist in evaluating ghost removal algorithms in HDR images. Optical flow algorithms have been shown to produce successful results in aligning input images before merging them into an HDR image. As a result one of the state-of-the-art deghosting algorithm for HDR image alignment is based on optical flow. To test the limits of the evaluated deghosting algorithms the scenes used in our experiments were selected following the criteria proposed by Baker et al. [2011], which is considered as de facto standard for evaluating optical flow methodologies. The scenes used in the experiments serve to provide challenges that need to be dealt with by not only algorithms based on optical flow methodologies but also by other ghost removal algorithms for HDR imaging. The results reveal the scenes for which the evaluated algorithms fail and may serve as a guide for future research in this area.

Exposure bracketing via automatic exposure selection

Abstract: Exposure bracketing is widely used to generate high dynamic range (HDR) images. Exposure bracketing often consists of an image taken by the auto-exposure setting of a camera as well as two other images taken at ±n exposure levels away from the auto-exposure setting where n is manually selected or specified. The images in the bracket are then blended to generate an HDR image. This paper presents an automatic exposure selection method that relieves the user from setting the exposure level, thus allowing HDR images to be generated with no user intervention. This is achieved by using the camera characteristics function and the scene information. The results obtained indicate the HDR images generated by using this method outperform the manual selection of exposure level in terms of two widely used image quality measures.