Tone-mapped Images Research Articles

High dynamic range image tone mapping based on variational image decomposition and color correction

Tone mapping aims to reproduce high dynamic range (HDR) images on low dynamic range (LDR) display devices. The layer-decomposition-based tone mapping algorithms usually decompose HDR images into base and detail layers and process them accordingly. Since the detail layer is not refined again, these algorithms may suffer from halo artifacts, relatively dull color, and over-enhancement problems. To solve these problems, we introduce the variational image decomposition model and color correction into the field of tone mapping, and propose a tone mapping algorithm that combines the three-part variational image decomposition model with a color correction scheme based on minimum color loss. In contrast to traditional layer-decomposition-based tone mapping algorithms, we employ the three-part decomposition model that decomposes the HDR image into three components: a cartoon component, a texture component, and a high-frequency detail component. The cartoon component is compressed using a gamma function, while the texture and high-frequency detail components are each enhanced using the nonlinear stretching functions. This approach allows for better detail enhancement while preserving the smoothness of the image. In addition, we further use the color correction scheme based on minimum color loss to solve the color problem caused by the layer-decomposition-based tone mapping algorithms. Experimental results show that our proposed tone mapping algorithm can reduce the halo artifacts and preserve the edge details while outperforming some state-of-the-art tone mapping algorithms.

Generic color correction for tone mapping operators in high dynamic range imaging.

One of the significant challenges in tone mapping is to preserve the perceptual quality of the tone-mapped images. Traditional tone mapping operators (TMOs) compress the luminance of high dynamic range (HDR) images with little to no consideration of image color information resulting into suboptimal colors. Existing color management algorithms require either manual parameter tuning or introducing lightness and hue shifts. The current study aimed to develop a color correction model in plausible agreement with the psychophysical phenomenon of color appearance perception for optimal color reproduction of tone mapped images. The proposed model leverages CIECAM16 perceptual correlates i.e., lightness, chroma, and hue. It is adaptive and entirely automatic while preserving the lightness of the tone-mapped image and maintaining the hue from the HDR image. Moreover, it does not require any gamut mapping correction algorithm in natural color reproduction. The effectiveness of the model was validated using objective and subjective methods. The experiments revealed that the model achieved optimal color reproduction in terms of the naturalness of the colors.

Contrastive learning for deep tone mapping operator

Most existing tone mapping operators (TMOs) are developed based on prior assumptions of human visual system, and they are known to be sensitive to hyperparameters. In this paper, we proposed a straightforward yet efficient framework to automatically learn the priors and perform tone mapping in an end-to-end manner. The proposed algorithm utilizes a contrastive learning framework to enforce the content consistency between high dynamic range (HDR) inputs and low dynamic range (LDR) outputs. Since contrastive learning aims at maximizing the mutual information across different domains, no paired images or labels are required in our algorithm. Equipped with an attention-based U-Net to alleviate the aliasing and halo artifacts, our algorithm can produce sharp and visually appealing images over various complex real-world scenes, indicating that the proposed algorithm can be used as a strong baseline for future HDR image tone mapping task. Extensive experiments as well as subjective evaluations demonstrated that the proposed algorithm outperforms the existing state-of-the-art algorithms qualitatively and quantitatively. The code is available at https://github.com/xslidi/CATMO.

Steering Kernel Weighted Guided Image Filtering with Gradient Constraint

In order to make the weighted guided image filter (WGIF) more fully utilize the edge direction of the image, a steering kernel weighted guided image filter (SKWGIF) is proposed by employing the steering kernel to learn the edge direction and incorporating the learning results into the WGIF. However, SKWGIF does not provide a good compromise between the two possibly contradictory objectives of edge-preserving and smoothing, where the image edges are inevitably smoothed. To overcome the drawback, a SKWGIF with gradient constraint (GC-SKWGIF) is proposed by introducing the gradient constraints into the SKWGIF. The gradient constraints allow the filter to take into account the gradient variation of the edges in the filtering process, and therefore the image edges can be better preserved. To verify the effectiveness of the proposed filter, the GC-SKWGIF is applied to edge-aware smoothing, tone mapping of high range dynamic images, image denoising and haze removal. Both theoretical analysis and experimental results show that the proposed filter can produce good resultant images.

Multi-Frame Content-Aware Mapping Network for Standard-Dynamic-Range to High-Dynamic-Range Television Artifact Removal.

Recently, advancements in image sensor technology have paved the way for the proliferation of high-dynamic-range television (HDRTV). Consequently, there has been a surge in demand for the conversion of standard-dynamic-range television (SDRTV) to HDRTV, especially due to the dearth of native HDRTV content. However, since SDRTV often comes with video encoding artifacts, SDRTV to HDRTV conversion often amplifies these encoding artifacts, thereby reducing the visual quality of the output video. To solve this problem, this paper proposes a multi-frame content-aware mapping network (MCMN), aiming to improve the performance of conversion from low-quality SDRTV to high-quality HDRTV. Specifically, we utilize the temporal spatial characteristics of videos to design a content-aware temporal spatial alignment module for the initial alignment of video features. In the feature prior extraction stage, we innovatively propose a hybrid prior extraction module, including cross-temporal priors, local spatial priors, and global spatial prior extraction. Finally, we design a temporal spatial transformation module to generate an improved tone mapping result. From time to space, from local to global, our method makes full use of multi-frame information to perform inverse tone mapping of single-frame images, while it is also able to better repair coding artifacts.

Optimal Weighted Modulus: A Secure and Large-Capacity Data-Hiding Algorithm for High Dynamic Range Images

This paper presents an optimal weighted modulus (OWM) algorithm able to conceal secret messages in a high dynamic range image encoded via the RGBE format, consisting of the red, green, blue, and exponent channels. In contrast to current state-of-the-art schemes, which mainly employ limited and vulnerable homogeneous representations, our OWM scheme exploits four channels and an embedding weight to conceal secret messages, thereby offering more embedding capacities and undetectability against steganalytic tools. To reduce the impact on the luminance variation, we confine the maximal change incurred in the exponent channel when embedding secret messages. In addition, we propose an SEC scheme to eliminate the pixel saturation problem, even though a pixel contains values close to the boundary extreme. As a result, the stego images produced not only exhibit high quality but also comply with the RGBE encoding format, making them able to resist malicious steganalytic detection. The experimental results show that our scheme offers larger embedding rates, between 2.8074 and 5.7549 bits per pixel, and the average PSNR value for twelve tone-mapped images is over 48 dB. In addition, the HDR VDP 3.0 metric demonstrates the high fidelity of stego HDR images, where the average Q value is close to the upper bound of 10.0. Our scheme can defeat RS steganalytic attacks and resist image compatibility attacks. A comparison result confirms that our scheme outperforms six current state-of-the-art schemes.

Quality Assessment of Tone-Mapped Images Using Fundamental Color and Structural Features

High dynamic range (HDR) images require tone-mapping to be viewed on low dynamic range (LDR) displays. The performance of tone-mapping algorithms can be evaluated through a subjective study in which participants based on their liking rank or score tone-mapped images (TMIs). Subjective evaluation can be painstakingly slow; therefore, several quantitative metrics have been proposed for objective evaluation. This paper presents a new robust metric that uses 16 features, measuring the loss of color, contrast, brightness, and structure, extracted from the test TMI and the reference HDR image. The effect of these attributes on image quality is investigated and combined into a single score in the [0, 1] range describing the quality of TMI. We validate the performance of the proposed metric by comparing it with 24 existing state-of-the-art metrics. The study uses two subjective datasets of TMIs, including one existing benchmark dataset and a new proposed dataset comprising HDR images of a variety of scenes, and a dataset of traditional images not generated through tone-mapping. In these studies, our method shows the highest correlation with subjective scores for both datasets of TMIs and remains in the second position for the dataset of traditional images.

XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-ray Security Inspection Images

XTC-DDPM: Diffusion Model for Tone Mapping and Pseudo-Color Imaging of Dual-Energy X-ray Security Inspection Images

CIECAM16-based Tone Mapping of High Dynamic Range Images

CIECAM16-based Tone Mapping of High Dynamic Range Images

Image tone mapping based on clustering and human visual system models

Natural scenes generally have very high dynamic range (HDR) which cannot be captured in the standard dynamic range (SDR) images. HDR imaging techniques can be used to capture these details in both dark and bright regions, and the resultant HDR images can be tone mapped to reproduce them on SDR displays. To adapt to different applications, the tone mapping operator (TMO) should be able to achieve high performance for diverse HDR scenes. In this paper, we present a clustering-based TMO by embedding human visual system models that function effectively in different scenes. A hierarchical scheme is applied for clustering to reduce the computational complexity. We also propose a detail preservation method by superimposing the details of original HDR images to enhance local contrasts, and a color preservation method by limiting the adaptive saturation parameter to control the color saturation attenuating. The effectiveness of our method is assessed by comparing with state-of-the-art TMOs quantitatively on large-scale HDR datasets and qualitatively with a group of subjects. Experimental results of both objective and subjective evaluations show that the proposed method achieves improvements over the competing methods in generating high quality tone-mapped images with good contrast and natural color appearance for diverse HDR scenes.

Pyramid Texture Filtering

We present a simple but effective technique to smooth out textures while preserving the prominent structures. Our method is built upon a key observation---the coarsest level in a Gaussian pyramid often naturally eliminates textures and summarizes the main image structures. This inspires our central idea for texture filtering, which is to progressively upsample the very low-resolution coarsest Gaussian pyramid level to a full-resolution texture smoothing result with well-preserved structures, under the guidance of each fine-scale Gaussian pyramid level and its associated Laplacian pyramid level. We show that our approach is effective to separate structure from texture of different scales, local contrasts, and forms, without degrading structures or introducing visual artifacts. We also demonstrate the applicability of our method on various applications including detail enhancement, image abstraction, HDR tone mapping, inverse halftoning, and LDR image enhancement. Code is available at https://rewindl.github.io/pyramid_texture_filtering/.

Tone Mapping Operator for High Dynamic Range Images Based on Modified iCAM06.

This study attempted to solve the problem of conventional standard display devices encountering difficulties in displaying high dynamic range (HDR) images by proposing a modified tone-mapping operator (TMO) based on the image color appearance model (iCAM06). The proposed model, called iCAM06-m, combined iCAM06 and a multi-scale enhancement algorithm to correct the chroma of images by compensating for saturation and hue drift. Subsequently, a subjective evaluation experiment was conducted to assess iCAM06-m considering other three TMOs by rating the tone mapped images. Finally, the objective and subjective evaluation results were compared and analyzed. The results confirmed the better performance of the proposed iCAM06-m. Furthermore, the chroma compensation effectively alleviated the problem of saturation reduction and hue drift in iCAM06 for HDR image tone-mapping. In addition, the introduction of multi-scale decomposition enhanced the image details and sharpness. Thus, the proposed algorithm can overcome the shortcomings of other algorithms and is a good candidate for a general purpose TMO.

Multi-Layer Decomposition and Synthesis of HDR Images to Improve High-Saturation Boundaries

Recently, high dynamic range (HDR) imaging has been used in many fields such as display, computer graphics, and digital cameras. Various tone mapping operators (TMOs) are used for effective HDR imaging. TMOs aim to express HDR images without loss of information and natural images on existing display equipment. In this paper, to improve the color distortion that occurs during tone mapping, multi-layer decomposition-based color compensation and global color enhancement of the boundary region are proposed. Multi-layer decomposition is used to preserve the color information of the input image and to find the area where color distortion occurs. Color compensation and enhancement are especially used to improve the color saturation of the border area, which is degraded due to color distortion and tone processing. Color compensation and enhancement are processed in IPT color space with excellent hue linearity to improve effective performance by reducing interference between luminance and chrominance components. The performance of the proposed method was compared to the existing methods using naturalness, distortion, and tone-mapped image quality metrics. The results show that the proposed method is superior to the existing methods.

High dynamic range in videodensitometry—a comparative study to classic videoscanning on Gentiana extracts

The advantages of high dynamic range (HDR) imaging in videodensitometry are presented and discussed on the example of Gentiana extract thin-layer fingerprints visualized under 254 nm. An inexpensive microscope camera, together with security surveillance lens, mounted instead of original camera on videodensitometry chamber, allows to grab HDR images with high tonal range using Python scripts and OpenCL library. HDR imaging preserves linearity in whole tonal range and does not destroy details in the brightest and darkest plate regions, so it can be seen as a good alternative to classical videodensitometry. Moreover, the tonemapping of HDR images can be used to present a plate photograph with enhanced visibility of weak spots and other details. Principal component analysis done on nine classic exposures and HDR image proves that HDR image contains the highest amount of extracted information from the thin-layer chromatographic plate.

Tone Mapping Method Based on the Least Squares Method

Tone mapping is used to compress the dynamic range of image data without distortion. To compress the dynamic range of HDR images and prevent halo artifacts, a tone mapping method is proposed based on the least squares method. Our method first uses weights for the estimation of the illumination, and the image detail layer is obtained by the Retinex model. Then, a global tone mapping function with the parameter is used to compress the dynamic range, and the parameter is obtained by fitting the function to the histogram equalization. Finally, the detail layer and the illumination layer are fused to obtain the LDR image. The experimental results show that the proposed method can efficiently restore real-world scene information while preventing halo artifacts. Therefore, tone mapping quality index and mean Weber contrast of the tone-mapped image are 8% and 12% higher than the closest competition tone mapping method.

Three-Stage Tone Mapping Algorithm

In this paper, a tone mapping algorithm is presented to map real-world luminance into displayed luminance. Our purpose is to reveal the local contrast of real-world scenes on a conventional monitor. Around this point, we propose a three-stage algorithm to visualize high dynamic range images. All pixels of high dynamic range images are classified into three groups. For the first stage, we introduce piecewise linear mapping as the global tone mapping operator to map the luminance of the first group, which provides overall impressions of luminance. For the second stage, the luminance of the second group is determined by the weighted average of its neighborhood pixels, which are derived from the first group’s pixels. For the third stage, the luminance of the third group is determined by the weighted average of its neighborhood pixels, which are derived from the second group’s pixels. Experimental results on several real-world images and the TMQI database show that our algorithm can improve the visibility of real-world scenes with about 12% and 9% higher scores of mean opinion score and tone-mapped image quality index than the closest competitive tone mapping methods. Compared to the existing tone mapping methods, our algorithm produces visually compelling results without halo artifacts and loss of detail.

No-reference quality index of tone-mapped images based on authenticity, preservation, and scene expressiveness

As a future-proof technology, high dynamic range (HDR) has the superiority in scene reappearance. Tone mapping operator (TMO) renders the dynamic effect of HDR images in a standard dynamic range (SDR) display at the expense of some insignificant information. However, it is difficult for existing TMOs to consistently achieve high-quality conversion of all HDR images, and the loss of converted images is inevitable. Therefore, accurate prediction of tone-mapped image (TMI) quality is of considerable significance. In this paper, we propose the reappearance effect of the TMIs index (RETI). According to the characteristics of primeval HDR images, the quality of TMIs is considered from three important elements: authenticity, the preservation of energy and information, and scene expressiveness. The feature vectors originated from three elements are combined with subjective quality to train prediction model. Experiments are executed on tow mainstream TMI quality assessment databases and the results show that the proposed method has a good prediction ability and generalization ability in comparison to some state-of-the-art methods. Our MATLAB source code will be released at https://github.com/niannianyouyu/RETI.

Adaptive method for image dynamic range adjustment and detail enhancement.

Tone mapping operators (TMOs) aim to adjust high dynamic range (HDR) images to low dynamic range (LDR) ones so that they can be displayed on conventional devices with visual information retained. Nonetheless, existing TMOs can successfully tone-map only limited types of HDR images, and the parameters need to be manually adjusted to yield the best subjective-quality tone-mapped outputs. To cope with the aforementioned issues, an adaptive parameter-free and scene-adaptive TMO for dynamic range adjusting and detail enhancing is proposed to yield a high-resolution and high-subjective-quality tone-mapped output. This method is based on detail/base layer decomposition to decompose the input HDR image into coarse detail, fine detail, and base images. After that, we adopt different strategies to process each layer to adjust the overall brightness and contrast and to retain as much scene information. Finally, a new method, to the best of our knowledge, is proposed for visualization to generate a sequence of artificial images to adjust the brightness. Experiments with numerous HDR images and state-of-the-art TMOs are conducted; the results demonstrate that the proposed method consistently produces better quality tone-mapped images than the state-of-the-art methods.

Blind quality assessment of tone-mapped images using multi-exposure sequences

The tone mapping operator (TMO) enables high dynamic range (HDR) images to be presented on low dynamic range (LDR) consumer electronic devices. However, the results obtained by this method are not always ideal due to the reduced number of bits. In comparison, the multi-exposure image fusion (MEF) bypasses the intermediate HDR image composition and directly produces an image presented on standard devices. Inspired by this, this paper proposes a quality assessment method for tone-mapped image (TMI) based on generating multi-exposure sequences. Specifically, the method uses a generative adversarial network (GAN) to generate a set of sequences with different exposure levels based on the TMIs. Then a two-branch convolutional neural network (CNN) is used to extract features from the tone-mapped images and the multi-exposure reference sequences, respectively. Finally, the transformer is used to mine the intrinsic connections between TMIs and multi-exposure sequences and learn the mapping relationships from feature space to quality space. We conducted extensive experiments on the ESPL-LIVE HDR database. The applicability and effectiveness of the proposed method are verified by comparing and analyzing relevant features and model configurations with existing mainstream evaluation algorithms.

47‐1: Display Outdoor Visibility Enhancement Using Adaptive Tone Mapping

In this document, we propose an image tone mapping method enhancing the image visibility in outdoor environment. The proposed method improves the outdoor visibility of the displayed image in two aspects: brightness enhancement and content adaptive contrast enhancement. From the experiment results, it is confirmed that the proposed method improves the brightness and the contrast at the same time and enhances outdoor visibility effectively even with the limited dynamic range of the display device. Compared to conventional methods, proposed method has higher flexibility and effectiveness. This method is already utilized on the mobile devices for outdoor visibility enhancement and we expect to apply it to the automotive platforms.