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.

BoostHDR: A novel backward-compatible method for HDR images

Abstract: In this paper, we present BoostHDR, a novel method for compressing high dynamic range (HDR) images. Thealgorithm leverages on a novel segmentation-based tone mapping operator (TMO) which relaxes the no seamsconstraint. Our method can work with both JPEG and JPEG2000 encoders. Moreover, it provides better resultscompared to the state of the art in HDR images compression algorithms in terms of bit per pixels (bpp), andvisual quality using objective metrics.Keywords: HDR Imaging, HDR Image Compression 1. INTRODUCTION The HDR content capturing is now becoming very popular, allowing consumers to capture HDR images withcompact and DLSR cameras or even mobile phones. Moreover, DSLRs can be used for capturing HDR videos, 12 and HDR video cameras are starting to emerge. 6,20,27 This new era in capturing allows users to represent thefull luminance range that the human visual system (HVS) can perceive.On the other hand, HDR content requires more memory for storing the extra dynamic range information thanconventional imaging at 8-bit or low dynamic range (LDR) imaging. For example, an uncompressed HDR pixel,represented using 32-bit oating point, can require four times the amount of memory of an uncompressed LDRpixel at 8-bit. This can negatively a ect performances too, because more bandwidth is needed. For instance, itwould be prohibitive to manage a photographic gallery of uncompressed HDR images or to play HDR videos.In recent years, algorithms for HDR content memory compression have been proposed. These methods aretypically based on existing compression standards such as JPEG, JPEG2000, and MPEG, which can be modi edor extended to handle HDR information. However, the community has not agreed on a common standardencoding yet. This is quite critical, because the HDR imaging has reached the market as extra feature or appfor cameras and mobile phones.In this paper, we present a novel compression algorithm which is based on an segmentation-based TMO, andexisting image compression standards such as JPEG and JPEG2000. Our key contributions are: Backward compatibility : the information in compressed images can be visualized by a JPEG or JPEG2000standard viewer. This allows users to visualize part of the content when software that can decode ourcompression algorithm is not present. Improvement over state of the art : our proposed solution provides better performances in terms of visualquality and bpp than JPEG-HDR compression by Ward and Simmmons,

Research on High Resolution and High Dynamic Range Image Reconstruction

Abstract: The goal of super resolution is to improve the spatial resolution of a image,while high dynamic range imaging algorithms can enhance the ability to describe the high contrast scenes in a image.The joint reconstruction study of both the high dynamic image and super resolution reconstruction has the great significance for the acquisition of the high quality image and can meet the needs of many multimedia applications.The progress in this area is investigated,the basic ideas and the difficulties in the implementation are analyzed.Furthermore, a framework of learning based joint reconstruction is proposed.

High dynamic range imaging method based on image content adaptive matrix completion

Abstract: High dynamic range (HDR) imaging usually produces ghosting artifacts, while the traditional matrix completion (MC) method may fail to completely remove the ghosts, without considering the motion characteristics of multi-exposure image. To solve this problem, this paper presents a new HDR imaging method based on content adaptive matrix completion of low dynamic range (LDR) image to remove the ghosts of HDR image. Firstly, according to the image luminance and chrominance information, the LDR image motion area is determined. Then, based on the priori information of motion, the regularization constraint intensity is adjusted in MC process to get each LDR image background information. Finally, a fusion strategy related to multiple exposures is proposed while the difference of details in each image area under different exposures is considered. Regular background sequences and cluttered background sequences are used for experiments. The experimental results demonstrate that, compared with the partial sum minimization of singular values-matrix completion method, the proposed method is more real-time and suitable for cluttered background sequences.

Hybrid High Dynamic Range Imaging fusing Neuromorphic and Conventional Images

Abstract: Reconstruction of high dynamic range image from a single low dynamic range image captured by a conventional RGB camera, which suffers from over- or under-exposure, is an ill-posed problem. In contrast, recent neuromorphic cameras like event camera and spike camera can record high dynamic range scenes in the form of intensity maps, but with much lower spatial resolution and no color information. In this article, we propose a hybrid imaging system (denoted as NeurImg) that captures and fuses the visual information from a neuromorphic camera and ordinary images from an RGB camera to reconstruct high-quality high dynamic range images and videos. The proposed NeurImg-HDR+ network consists of specially designed modules, which bridges the domain gaps on resolution, dynamic range, and color representation between two types of sensors and images to reconstruct high-resolution, high dynamic range images and videos. We capture a test dataset of hybrid signals on various HDR scenes using the hybrid camera, and analyze the advantages of the proposed fusing strategy by comparing it to state-of-the-art inverse tone mapping methods and merging two low dynamic range images approaches. Quantitative and qualitative experiments on both synthetic data and real-world scenarios demonstrate the effectiveness of the proposed hybrid high dynamic range imaging system. Code and dataset can be found at: https://github.com/hjynwa/NeurImg-HDR.