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.

Edge-guided filtering scheme for decomposition-based tone mapping

Abstract: This paper presents a novel edge-guided filtering scheme for decomposition-based tone mapping, whose superiority is to prevent two major defects in filter-driven multi-scale decomposition: halo artifact and over-smoothing distortion. First, we calculate an edge-preserving smoothing by gradient domain reconstruction with given edges. Then we apply this output in high dynamic range tone mapping to address aforementioned problems. At last, some experimental results are presented to demonstrate the effectiveness of our method in producing high-quality low dynamic range outputs.

A NR-IQA based deep neural network for tone mapping HDR images

Abstract: The most recent High Dynamic Range (HDR) standard, HDR10+, achieves good picture quality by incorporating dynamic metadata that carry frame-by-frame information for tone mapping while most HDR standards use static tone mapping curves that apply across the entire video. Since it is laborious to acquire hand-crafted best-fitting tone mapping curve for each frame, there have been attempts to derive the curves from input images. This paper proposes the neural network framework that generates tone mapping on a frame-by-frame basis. Although a number of successful tone mapping operators (TMOs) have been proposed over the years, evaluation of tone mapped images still remains a challenging topic. We define an objective measure to evaluate tone mapping based on Non-Reference Image Quality Assessment (NR-IQA). Experiments show that the framework produces good tone mapping curves and makes the video more vivid and colorful.

An optimizing processing approach to contrast correction based on nonlinear mapping of windowed tone

Abstract: A contrast correction method is presented based on nonlinear mapping of windowed tone. The main idea of method is to employ the local nonlinear mapping model on the small size with overlapping windows of traversal the whole image. At first, a high dynamic range (HDR) image contrast correction is introduced, and then through the formula deduction, a model for decision optimization of contrast correction is established, in which some constraints are termed as two adaptive guided images based on human visual properties so as to improve the optimal solution. Finally, the optimal contrast correction can be implemented by solving the optimizing processing problem through a linearized reduction. A series of experiments with the HDR natural images are carried out and the results of objective quality metrics have showed that the proposed method can effectively improve and optimize the contrast correction to outperform those current existing methods.

Interval quality indicators of the dynamic range compression of infrared images on the basis of a tone mapping matrix

Abstract: The article proposes a dynamic range compression model for infrared (IR) images based on a tone mapping matrix, the elements of which relate the brightness levels of the original image with a wide dynamic range and the brightness levels of a non-linearly transformed image with a narrow dynamic range, and also indicate, depending on the variant of formation of this matrices on: a) loss of discrimination between adjacent pixels due to compression of the dynamic range; b) the level of non-linear compression distortions; c) ambiguity of tone mapping. Based on this model, interval indicators of the quality of compression of the dynamic range of infrared images are proposed, which allow estimating the potential distinguishing power, the real loss of discrimination between adjacent pixels after transformation, the magnitude of nonlinear compression distortions, the uniformity of the use of the dynamic range, and the ambiguity of tone mapping for the selected interval of the dynamic range. The proposed indicators improve the accuracy of assessing the quality of compression of the dynamic range of IR images by expanding the system of known indicators that evaluate the contrast, entropy, statistical naturalness of the converted images, and the structural accuracy of tone mapping.

HDR Imaging with Spatially Varying Signal-to-Noise Ratios

Abstract: While today's high dynamic range (HDR) image fusion algorithms are capable of blending multiple exposures, the acquisition is often controlled so that the dynamic range within one exposure is narrow. For HDR imaging in photon-limited situations, the dynamic range can be enormous and the noise within one exposure is spatially varying. Existing image denoising algorithms and HDR fusion algorithms both fail to handle this situation, leading to severe limitations in low-light HDR imaging. This paper presents two contributions. Firstly, we identify the source of the problem. We find that the issue is associated with the co-existence of (1) spatially varying signal-to-noise ratio, especially the excessive noise due to very dark regions, and (2) a wide luminance range within each exposure. We show that while the issue can be handled by a bank of denoisers, the complexity is high. Secondly, we propose a new method called the spatially varying high dynamic range (SV-HDR) fusion network to simultaneously denoise and fuse images. We introduce a new exposure-shared block within our custom-designed multi-scale transformer framework. In a variety of testing conditions, the performance of the proposed SV-HDR is better than the existing methods.