Tone mapping

About: Tone mapping is a research topic. Over the lifetime, 1713 publications have been published within this topic receiving 48490 citations.

Detail Restoration and Tone Mapping Networks for X-Ray Security Inspection

Abstract: X-ray imaging is one of the most widely used security measures for maintaining airport and transportation security. Conventional X-ray imaging systems typically apply tone-mapping (TM) algorithms to visualize high-dynamic-range (HDR) X-ray images on a standard 8-bit display device. However, X-ray images obtained through traditional TM algorithms often suffer from halo artifacts or detail loss in inter-object overlapping regions, which makes it difficult for an inspector to detect unsafe or hazardous objects. To alleviate these problems, this article proposes a deep learning-based TM method for X-ray inspection. The proposed method consists of two networks called detail-recovery network (DR-Net) and TM network (TM-Net). The goal of DR-Net is to restore the details in the input HDR image, whereas TM-Net aims to compress the dynamic range while preserving the restored details and preventing halo artifacts. Since there are no standard ground-truth images available for the TM of X-ray images, we propose a novel loss function for unsupervised learning of TM-Net. We also introduce a dataset synthesis technique using the Beer-Lambert law for supervised learning of DR-Net. Extensive experiments comparing the performance of our proposed method with state-of-the-art TM methods demonstrate that the proposed method not only achieves visually compelling results but also improves the quantitative performance measures such as FSITM and HDR-VDP-2.2.

Methods, apparatus, and systems for hdr tone mapping operator

Abstract: Aspects of present principles are directed to methods and apparatus for tone mapping a high dynamic range image. The apparatus includes a processor for performing the following and the method includes the following: obtaining a luminance component of the high dynamic range image; determining an hdr-to-hdr tone mapper curve; determining a tone compressed image by applying the hdr-to-hdr tone mapper curve to the luminance component of the high dynamic range image; wherein the hdr-to-hdr tone mapper curve comprises a linear part for dark and mid-tone levels, and a compressive non-linear part for highlights.

Multi Exposure Image Fusion

Abstract: High dynamic range images are images that have a ratio between their maximum and minimum intensities greater than that of display or capture devices. One method to create high dynamic range images is to combine a set of differently exposed shoots into a single image that has to be compressed before visualization. Compressing high dynamic range images is thus based on a so called high dynamic range map (HDR). In this paper we show that a solution to the problem can be obtained without the need for the calculation of HDR maps. To achieve this we use an image fusion method in the gradient domain. We start by calculating a gradient field which encompasses the details found in the exposure image series. This gradient field is then integrated to result in the final within dynamic range image. The resulting algorithm achieves high image contrast due to a non-linear thresholding of gradients.

Chromatic adaptation-based tone reproduction for high-dynamic-range imaging

Abstract: We present an adaptive tone reproduction algorithm for displaying high-dynamic-range (HDR) images on conventional low-dynamic-range (LDR) display devices. The proposed algorithm consists of an adaptive tone reproduction operator and chromatic adaptation. The algorithm for dynamic range reduction relies on suitable tone reproduction functions that depend on histogram-based parameter estimation to adjust luminance according to global and local features. Instead of relying only on reduction of dynamic range, this chromatic adaption technique also preserves chromatic appearance and color consistency across scene and display environments. Our experimental results demonstrate that the proposed algorithm achieves good subjective quality while preserving image details. Furthermore, the proposed algorithm is simple and practical for implementation.

Obstacle Distance Measurement Under Varying Illumination Conditions Based on Monocular Vision Using a Cable Inspection Robot

Abstract: Obstacle distance measurement is one of the key technologies for autonomous navigation of high-voltage transmission line inspection robots. To address the robustness of obstacle distance measurement under varying illumination conditions, this article develops a research method that fuses image enhancement with robot monocular vision so that the robot can adapt to various levels of illumination running along the transmission line. During the inspection of high-voltage transmission lines in such an overexposed (excessively bright) environment, a specular highlight suppression method is proposed to suppress the specular reflections in an image; when scene illumination is insufficient, a robust low-light image enhancement method based on a tone mapping algorithm with weighted guided filtering is presented. Based on the monocular vision measurement principle, the error generation mechanism is analyzed through experiments, and we introduce the parameter modification mechanism. The two proposed image enhancement methods outperform other state-of-the-art enhancement algorithms in qualitative and quantitative analyses. The experimental results show that the measurement error is less than 3% for static distance measurements and less than 5% for dynamic distance measurements within 6 m. The proposed method can meet the requirements of high-accuracy positioning, real-time performance and strong robustness. This method greatly contributes to the sustainable development of inspection robots in the power industry.