HDR Imaging Pipeline for Spectral Filter Array Cameras

Technique of high dynamic range imaging (HDRI) was introduced into conventional high dynamic range display (HDRD). Sharpness of image was further enhanced by improving local contrast ratio in the HDRI-based high dynamic range display.

Perceptual brightness-based inverse tone mapping for high dynamic range imaging

Most recent bidirectional reflectance distribution function (BRDF) measurement systems are the image-based that consist of a light source, a detector, and curved samples. They are useful for measuring the reflectance properties of a material but they have two major drawbacks. They suffer from high cost of BRDF acquisition and also give inaccurate results due to the limited use of spectral bands. In this paper, we propose a novel multispectral HDR imaging system and its efficient characterization method. It combines two promising technologies: high dynamic range (HDR) imaging and multispectral imaging to measure BRDF. We perform a full spectral recovery using camera response curves for each wavelength band and its analysis. For this, we use an HDR camera to capture HDR images and a liquid crystal tunable filter (LCTF) to generate multi-spectral images. Our method can provide an accurate color reproduction of metameric objects as well as a saturated image. Our multi-spectral HDR imaging system provides a very fast data acquisition time and also gives a low system setup cost compared to previous multi-spectral imaging systems and point-based commercial spectroradiometers. We verify the color accuracy of our multi-spectral HDR imaging system in terms of human vision and metamerism using colorimetric and spectral metric.

Scenes in real world have dynamic range of radiation that cannot be captured by conventional cameras. High dynamic range imaging is a technique to capture detail images where, in the field of image, intensity variation is extreme. This technique is very useful for biological imaging where the samples have very bright and very dark regions and both parts have useful information. In this article we propose a novel high dynamic range imaging technique based on compressive imaging that uses one single detector instead of camera (array of detectors) to capture an image. Combination of high dynamic range imaging and compressive imaging benefits from imaging with high dynamic range of radiation and advantages of compressive sampling; namely, imaging at regions of optical spectrum where conventional cameras are not readily available and single detectors are available. Additionally, as its name suggests, this technique requires less number of samples (compared to raster scanning). Our experimental results show that high dynamic range compressive imaging system is capable of capturing images with large intensity contrast.

High dynamic range imaging techniques involve capturing and storing real world radiance values that span many orders of magnitude. However, common display devices can usually reproduce intensity ranges only up to two to three orders of magnitude. Therefore, in order to display a high dynamic range image on a low dynamic range screen, the dynamic range of the image needs to be compressed without losing details or introducing artefacts, and this process is called tone mapping. A good tone mapping operator must be able to produce a low dynamic range image that matches as much as possible the perception of the real world scene. We propose a two stage tone mapping approach, in which the first stage is a global method for range compression based on a gamma curve that equalizes the lightness histogram the best, and the second stage performs local contrast enhancement and color induction using neural activity models for the visual cortex. © (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.

High dynamic range imaging (HDRI) methods in computational photography address situations where the dynamic range of a scene exceeds what can be captured by an image sensor in a single exposure. HDRI techniques have also been used to construct radiance maps in measurement applications; unfortunately, the design and evaluation of HDRI algorithms for use in these applications have received little attention. In this paper, we develop a novel HDRI technique based on pixel-by-pixel Kalman filtering and evaluate its performance using objective metrics that this paper also introduces. In the presented experiments, this new technique achieves as much as 9.4-dB improvement in signal-to-noise ratio and can achieve as much as a 29% improvement in radiometric accuracy over a classic method.

The luminous value is high for many natural scenes, which causes loss of information and occurs in dark images. The High Dynamic Range (HDR) technique captures the same objects or scene for multiple times in different exposure and produces the images with proper illumination. This technique is used in the various applications such as medical imaging and observing the skylight, etc. HDR imaging techniques usually have the issue of lower efficiency due to capturing of multiple photos. In this paper, an efficient method is proposed for HDR imaging technique to achieve better performance and lower noise. The Luminance-Chrominance-Gradient High Dynamic Range (LCGHDR) method is proposed to obtain the proper luminous value of images. The same scenario is captured at different exposure are processed by the proposed method. Based on these feature values extracted from the different images and exposure fusion technique was developed that helps for the proper imaging. This experiment was evaluated and analyzed by comparing with the other methods, which showed the efficiency of the proposed method. This method needs only 124.594 seconds for the computation, while existing method need 139.869 seconds for the same number of images.

An accurate characterization method for a multispectral high-dynamic-range (HDR) imaging system is proposed by combining multispectral and HDR imaging technologies. The multispectral HDR imaging system, which can acquire the visible spectrum at many wavelength bands, can provide an accurate color reproduction and physical radiance information of real objects. An HDR camera is used to capture an HDR image without multiple exposures and a liquid crystal tunable filter (LCTF) is used to generate multispectral images. Due to its several limitations in the multispectral HDR imaging system, a carefully designed and an innovative characterization algorithm is presented by considering a logarithmic camera response of the HDR camera and different spectral transmittance of the LCTF. The proposed method efficiently and accurately recovers the full spectrum from the multispectral HDR images using a transformation matrix and provides device-independent color information (e.g., CIEXYZ and CIELAB). The transformation matrix is estimated by training the estimated sensor responses from a multispectral HDR imaging system and the reflectance measurements from a spectroradiometer using Moore–Penrose pseudoinverse matrix.

Ghost detection and removal for high dynamic range images: Recent advances

International audience

This paper evaluates the suitability of High Dynamic Range (HDR) imaging techniques for feature point detection under extreme lighting conditions. The conditions are extreme in respect to the dynamic range of the lighting within the test scenes used. This dynamic range cannot be captured using standard low dynamic range imagery techniques without loss of detail. Four widely used feature point detectors are used in the experiments: Harris corner detector, Shi-Tomasi, FAST and Fast Hessian. Their repeatability rate is studied under changes of camera viewpoint, camera distance and scene lighting with respect to the image formats used. The results of the experiments show that HDR imaging techniques improve the repeatability rate of feature point detectors significantly.

Conventional High Dynamic Range (HDR) image fusion algorithms generally require two or more original images with different exposure times for synthesis, making them unsuitable for real-time processing scenarios such as video streams. Additionally, the synthesized HDR images have the same bit depth as the original images, which may lead to banding artifacts and limits their applicability in professional fields requiring high fidelity. This paper utilizes a Field Programmable Gate Array (FPGA) to support an image sensor operating in Clear HDR mode, which simultaneously outputs High Conversion Gain (HCG) and Low Conversion Gain (LCG) images. These two images share the same exposure duration and are captured at the same moment, making them well-suited for real-time HDR fusion. This approach provides a feasible solution for real-time processing of video streams. An adaptive adjustment algorithm is employed to address the requirement for high fidelity. First, the initial HCG and LCG images are fused under the initial fusion parameters to generate a preliminary HDR image. Subsequently, the gain of the high-gain images in the video stream is adaptively adjusted according to the brightness of the fused HDR image, enabling stable brightness under dynamic illumination conditions. Finally, by evaluating the read noise of the HCG and LCG images, the fusion parameters are adaptively optimized to synthesize an HDR image with higher bit depth. Experimental results demonstrate that the proposed method achieves a processing rate of 46 frames per second for 2688 × 1520 resolution video streams, enabling real-time processing. The bit depth of the image is enhanced from 12 bits to 16 bits, preserving more scene information and effectively addressing banding artifacts in HDR images. This improvement provides greater flexibility for subsequent image processing tasks. Consequently, the adaptive algorithm is particularly suitable for dynamically changing scenarios such as real-time surveillance and professional applications including industrial inspection.

In this paper, we propose a bit-depth scalable lossless coding method for high dynamic range (HDR) images based on a reversible logarithmic mapping. HDR images are generally expressed as floating-point data, such as in the OpenEXR or RGBE formats. Our bit-depth scalable coding approach outputs base layer data and enhancement layer data. It can reconstruct the low dynamic range (LDR) image from the base layer data and reconstructs the HDR image by adding the enhancement layer data. Most previous two-layer methods have focused on the lossy coding of HDR images. Unfortunately, the extension of previous lossy methods to lossless coding does not significantly compress the enhancement layer data. This is because the bit depth becomes very large, especially for HDR images in floating-point data format. To tackle this problem, we apply a reversible logarithmic mapping to the input HDR data. Moreover, we introduce a format conversion to avoid any degradation in the quality of the reconstructed LDR image. The proposed method is effective for both OpenEXR and RGBE formats. Through a series of experiments, we confirm that the proposed method decreases the volume of compressed data while maintaining the visual quality of the reconstructed LDR images.

High dynamic range (HDR) images are becoming pervasive due to capturing or rendering of a wider range of luminance, but their special display equipment is difficult to be popularized because of high cost and technological problem. Thus, HDR images must be adapted to the conventional display devices by applying tone mapping (TM) operation, which puts forward higher requirements for intellectual property protection of HDR images. As the robustness presents regional diversity in the low dynamic range (LDR) watermarked image after TM, which is different from the traditional watermarking technologies, a concept of watermarking activity is defined and used to distinguish the essential distinction of watermarking between LDR image and HDR image in this paper. Then, a novel robust HDR image watermarking algorithm is proposed against TM operations. Firstly, based on the hybrid processing of redundant discrete wavelet transform and singular value decomposition, the watermark is embedded by modifying the structure information of the HDR image. Distinguished from LDR image watermarking, the high embedding strength can cause more obvious distortion in the high brightness regions of HDR image than the low brightness regions. Thus, a perceptual brightness mask with low complexity is designed to improve the imperceptibility further. Experimental results show that the proposed algorithm is robust to the existing TM operations, with taking into account the imperceptibility and embedded capacity, which is superior to the current state-of-art HDR image watermarking algorithms.

Traditional spectral imaging systems mainly rely on spatial scanning or spectral scanning methods to acquire spatial and spectral features. The acquisition is time-consuming and cannot fully satisfy the need of monitoring dynamic phenomenon and observing different structures of the specimen simultaneously. To overcome these barriers, we develop a video-rate simultaneous multispectral imaging system built with a spectral multiplexed volume holographic grating (VHG) and few optical components. Four spectral multiplexed volume holograms optimized for four discrete spectral bands (centered at 488 nm, 530 nm, 590 nm and 620 nm) are recorded into an 8×12 mm photo-thermal refractive glass. The diffraction efficiencies of all the holograms within the multiplexed VHG are greater than 80%. With the high throughout multiplexed VHG, the system can work with both reflection and fluorescence modes and allow simultaneous acquisition of spectral and spatial information with a single exposure. Imaging experiments demonstrate that the multispectral images of the target illuminated with white light source can be obtained. Fluorescence images of multiple fluorescence objects (two glass beads filled with 20 uL 1.0 mg/mL quantum dots solutions that emit 530 ± 15 nm and 620 ± 15 nm fluorescence, respectively) buried 3 mm below the surface of a tissue mimicking phantom are acquired. The results demonstrate that the system can provide complementary information in fluorescence imaging. The design diagram of the proposed system is given to explain the advantage of compactness and flexibility in integrating with other imaging platforms.