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.

Exposure-Structure Blending Network for High Dynamic Range Imaging of Dynamic Scenes

Abstract: This paper presents a deep end-to-end network for high dynamic range (HDR) imaging of dynamic scenes with background and foreground motions Generating an HDR image from a sequence of multi-exposure images is a challenging process when the images have misalignments by being taken in a dynamic situation Hence, recent methods first align the multi-exposure images to the reference by using patch matching, optical flow, homography transformation, or attention module before the merging In this paper, we propose a deep network that synthesizes the aligned images as a result of blending the information from multi-exposure images, because explicitly aligning photos with different exposures is inherently a difficult problem Specifically, the proposed network generates under/over-exposure images that are structurally aligned to the reference, by blending all the information from the dynamic multi-exposure images Our primary idea is that blending two images in the deep-feature-domain is effective for synthesizing multi-exposure images that are structurally aligned to the reference, resulting in better-aligned images than the pixel-domain blending or geometric transformation methods Specifically, our alignment network consists of a two-way encoder for extracting features from two images separately, several convolution layers for blending deep features, and a decoder for constructing the aligned images The proposed network is shown to generate the aligned images with a wide range of exposure differences very well and thus can be effectively used for the HDR imaging of dynamic scenes Moreover, by adding a simple merging network after the alignment network and training the overall system end-to-end, we obtain a performance gain compared to the recent state-of-the-art methods

Tone mapping with contrast preservation and lightness correction in high dynamic range imaging

Abstract: In real-world environments, the human visual system perceives a wide range of luminance in a scene. However, the full range of tones in a high dynamic range (HDR) scene cannot be displayed on standard display devices due to their low dynamic range (LDR). Therefore, tone mapping is necessary to faithfully display a HDR scene on an LDR display device. Existing tone mapping methods have problems because details and contrast in a scene are not preserved faithfully, and they also distort the colors in a scene. Thus, we propose a tone mapping method for preserving the detail in an HDR scene using a weighted least squares filter, which preserves the global contrast in a scene by using a competitive learning neural network, before applying a tone reproduction operator to preserve the color without shifting the lightness. According to the Helmholtz–Kohlrausch effect, the perception of brightness depends on the lightness, chroma, and hue of a color. For example, the luminance of pixels with specific colors such as red and blue is low in an HDR scene. The proposed method corrects the lightness of pixels according to the color (i.e., lightness, chroma, and hue) of a tone-mapped image. Experimental results with several test sets of images demonstrated that the proposed tone mapping method with contrast preservation and lightness correction is more suitable for dynamic range compression than existing tone mapping methods, while it also preserves the color of a scene in an effective way.

Noise in high dynamic range imaging

Abstract: High dynamic range (HDR) imaging is more and more widely used to increase the limited dynamic range of digital cameras and, in turn, to cover the dynamic range of the acquired scene. This image acquisition process can be subdivided into two steps. The first step is the measurement or estimation of the mostly non-linear camera transfer function (CTF). This is followed by the second step, the combination of a set of differently exposed images of the same scene into one HDR image after linearization with the inverse CTF. Each of the individual images in such an exposure set contains noise from the image acquisition process. Consequently, the calculated HDR image will as well contain noise, which fortunately is reduced by the weighted average of the images from the exposure set. We analyze the achieved gain in SNR for different weighting functions proposed in the literature and compare these with a plain average. Although these functions are based on reasonable intuitions, we show that the highest SNRgain is achieved with the plain average.

Subjective evaluation of tone-mapping methods on 3D images

Abstract: High dynamic range (HDR) imaging provides superior picture quality to traditional 8 bit, low dynamic range (LDR), image representations. Capturing images/videos in HDR format can avoid problems with over and under exposures. Tone-mapping is a process that converts from HDR to LDR, so that HDR content can be shown on existing displays. Tone mapping has been extensively studied in the context of 2D images/video but not for 3D content. This paper addresses the problem of presenting 3D HDR content on stereoscopic LDR displays and presents a subjective psychophysical experiment that evaluates existing tone-mapping operators on 3D HDR images. The results show that 3D content derived using tone-mapping is much preferred to that is captured directly with a pair of LDR cameras. Global tone-mapping methods (which better preserve global contrast) are found to produce images with better 3D effect than local tone-mapping operators (which produce images with high amounts of detail/texture). Also, the brightness of the tone-mapped images is found to be highly collated with perceived 3D quality.

High dynamic range imaging pixels with improved readout

Abstract: An imaging system may include an image sensor having an array of dual gain pixels. Each pixel may be operated using a two read method such that all signals are read in a high gain configuration in order to improve the speed or to reduce the power consumption of imaging operations. Each pixel may be operated using a two read, two analog-to-digital conversion method in which two sets of calibration data are stored. A high dynamic range (HDR) image signal may be produced for each pixel based on signals read from the pixel and on light conditions. The HDR image may be produced based on a combination of high and low gain signals and one or both of the two sets of calibration data. A system of equations may be used for generating the HDR image. The system of equations may include functions of light intensity.