Global Tone Mapping Research Articles

The feature enhancement method of artistic images based on histogram equalization and bilateral filtering

To improve the rendering effect of artistic images, a method enhancing features of artistic images is proposed based on histogram equalization and bilateral filtering in the article. Firstly, artistic images are divided into both high and low-frequency representations, and the multi-step enhancement processing level is delimited by multi-band decomposition. Secondly, the noise in the image is removed by bilateral filtering. Then, the grey-level histogram of the image is modified by using the histogram equalization. Finally, the features of the artistic image are enhanced by global tone mapping after histogram equalization processing is conducted. Then, the image is sharpened to improve the enhancement effect further. The experiments show that the features of the color and edge details turn out to be more vivid and clearer after the proposed method is implemented. The structural similarity (SSIM) measure of the image increases to 0.973, and the average gradient gets close to 0.8, which shows that the proposed method is effective.

Brightness segmentation-based plateau histogram equalization algorithm for displaying high dynamic range infrared images

Third-generation thermal cameras produce images with high dynamic range (HDR), low contrast, and blurry edges, which makes them difficult to visualize on traditional display devices. Thus, tone mapping methods are required to adapt the recorded signal to the display in order to maintain, and possibly improve, object’s visibility and contrast. Since the traditional global tone mapping methods cannot take account of the trade-off between clearly displaying the details of dark regions and bright regions of the infrared image, this paper proposes a global tone mapping algorithm based on brightness segmentation for the enhancement and display of HDR infrared images. Firstly, the HDR infrared image is divided into different brightness regions in accordance with the characteristics of the human visual system’s ability to perceive brightness. Secondly, the determination of the boundary value of each brightness region is optimized by combining the gray statistics of the HDR infrared image, to achieve the adaptive brightness region segmentation of the infrared image from various scenes. Thirdly, the plateau histogram equalization method is designed to enhance different brightness regions separately, according to their brightness levels. Finally, the enhanced brightness regions are recombined to produce a more visually pleasing output image. The effectiveness of the proposed method is analyzed of HDR infrared images taken from different scenes. A comparison of the proposed method with well-established global tone mapping techniques is provided, along with subjective and objective evaluation methods that demonstrate the potential benefits of this method in enhancing brightness, compressing dynamic range, and improving global contrast.

Weibull Tone Mapping (WTM) for the Enhancement of Underwater Imagery.

Domain experts prefer interactive and targeted control-point tone mapping operations (TMOs) to enhance underwater image quality and feature visibility; though this comes at the expense of time and training. In this paper, we provide end-users with a simpler and faster interactive tone-mapping approach. This is built upon Weibull Tone Mapping (WTM) theory; introduced in previous work as a preferred tool to describe and improve domain expert TMOs. We allow end-users to easily shape brightness distributions according to the Weibull distribution, using two parameter sliders which modify the distribution peak and spread. Our experiments showed that 10 domain experts found the two-slider Weibull manipulation sufficed to make a desired adjustment in >80% of images in a large dataset. For the remaining ∼20%, observers opted for a control-point TMO which can, broadly, encompass many global tone mapping algorithms. Importantly, 91% of these control-point TMOs can actually be visually well-approximated by our Weibull slider manipulation, despite users not identifying slider parameters themselves. Our work stresses the benefit of the Weibull distribution and significance of image purpose in underwater image enhancement.

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.

Learning Frequency Domain Priors for Image Demoireing

Image demoireing is a multi-faceted image restoration task involving both moire pattern removal and color restoration. In this paper, we raise a general degradation model to describe an image contaminated by moire patterns, and propose a novel multi-scale bandpass convolutional neural network (MBCNN) for single image demoireing. For moire pattern removal, we propose a multi-block-size learnable bandpass filters (M-LBFs), based on a block-wise frequency domain transform, to learn the frequency domain priors of moire patterns. We also introduce a new loss function named Dilated Advanced Sobel loss (D-ASL) to better sense the frequency information. For color restoration, we propose a two-step tone mapping strategy, which first applies a global tone mapping to correct for a global color shift, and then performs local fine tuning of the color per pixel. To determine the most appropriate frequency domain transform, we investigate several transforms including DCT, DFT, DWT, learnable non-linear transform and learnable orthogonal transform. We finally adopt the DCT. Our basic model won the AIM2019 demoireing challenge. Experimental results on three public datasets show that our method outperforms state-of-the-art methods by a large margin.

Layer Decomposition based Local Tone Mapping Operator

Dynamic range of an image directly defines the number of levels it can accommodate in an image. In general, a dynamic range of 256 is in use. Defining dynamic range of an image need to be done by considering the display unit on which the image will be displayed on. If the dynamic range extends over 256, the dynamic range is said to be high dynamic range. This range may extend up to 10,000. This kind of images can’t be displayed on display units which can’t differentiate that many pictorial information. This kind of pictorial information need to be converted so that the regular display units can adapt the format. This must be done without losing much information. This process is very crucial and is done by several tone mapping operators. Tone mapping operators are of two types, global and local. Global tone mapping operators apply same mapping function throughout the image while the local tone mapping operators use different mapping functions for local regions of the image. Though the quality of global TMOs is better, there are many halo effects in the converted image. In this paper, a global TMO based on decomposition is proposed intended to reduce these effects. Image is decomposed in to two layers, base, and detail. A hybrid decomposition and optimization are proposed to improve the quality of converted image.

Real-Time Deep Image Retouching Based on Learnt Semantics Dependent Global Transforms.

Although artists' actions in photo retouching appear to be highly nonlinear in nature and very difficult to characterize analytically, we find that the net effects of interactively editing a mundane image to a desired appearance can be modeled, in most cases, by a parametric monotonically non-decreasing global tone mapping function in the luminance axis and by a global affine transform in the chrominance plane that are weighted by saliency. This allows us to simplify the machine learning problem of mimicking artists in photo retouching to constructing a deep artful image transform (DAIT) using convolutional neural networks (CNN). The CNN design of DAIT aims to learn the image-dependent parameters of the luminance tone mapping function and the affine chrominance transform, rather than learning the end-to-end pixel level mapping as in the mainstream methods of image restoration and enhancement. The proposed DAIT approach reduces the computation complexity of the neural network by two orders of magnitude, which also, as a side benefit, improves the robustness and generalization capability at the inference stage. The high throughput and robustness of DAIT lend itself readily to real-time video enhancement as well after a simple temporal processing. Experiments and a Turing-type test are conducted to evaluate the proposed method and its competitors.

Tone mapping high dynamic 3D scenes with global lightness coherency

We propose a new approach for real-time Tone Mapping Operator dedicated to High Dynamic Range rendering of interactive 3D scenes. The proposed method considers the whole scene lighting in order to preserve the global coherency. This is the major contribution of our method. Indeed, most of existing Tone Mapping Operators only consider the image rendered by the camera at the current frame and simulate the Human Visual System accommodation to bright and dark luminance. Consequently, after an adaptation time, the lighting design of the 3D scene is lost. For example, two rooms with a high contrast (one dark and one bright) can be perceived with the same luminance level after adaptation. To cope with this coherency issue, we adapt an existing Tone Mapping Operator that combines (1) a global Tone Mapping Operator which takes into account the High Dynamic Range of the whole scene and (2) a viewport frame based Tone Mapping Operator that enhances the image contrast. Thus, we preserve the global lighting coherency and enhance the contrast for each rendered image. Furthermore, we present a subjective evaluation that shows that our method provides a better user experience than previous methods in the case of visualization on Head-Mounted Display.

80‐4: Human Visual System Uses Just a Few Transfer Functions Depending on Various Environments to Realize Normalized Visual Percept: Investigation Using Real Photographic Images

We investigate global tone mapping operators (GTMOs) that convert physical stimuli in various real HDR environments (i.e., luminance contrast ratio (CR) is up to approx. 105:1) into normalized visual percept (NVP) by actually comparing real scenes with reproduced photographic images. Subjective test results suggest that the images reproduced by our proposed method are successfully converted into SDR (i.e., CR is approx. 102:1). We also demonstrate that these NVPs recorded in SDR images can be re‐converted into LDR images (i.e., CR is approx. 10:1) while preserving natural tone.

Tone Mapping of High Dynamic Range Images Combining Co-Occurrence Histogram and Visual Salience Detection

One of the significant qualities of the human vision, which differentiates it from computer vision, is so called attentional control, which is the innate ability of our human eyes to select what visual stimuli to pay attention to at any moment in time. In this sense, the visual salience detection model, which is designed to simulate how the human visual system (HVS) perceives objects and scenes, is widely used for performing multiple vision tasks. This model is also in high demand in the tone mapping technology of high dynamic range images (HDRIs). Another distinct quality of the HVS is that our eyes blink and adjust brightness when objects are in their sight. Likewise, HDR imaging is a technology applied to a camera that takes pictures of an object several times by repeatedly opening and closing a camera iris, which is referred to as multiple exposures. In this way, the computer vision is able to control brightness and depict a range of light intensities. HDRIs are the product of HDR imaging. This article proposes a novel tone mapping method using CCH-based saliency-aware weighting and edge-aware weighting methods to efficiently detect image salience information in the given HDRIs. The two weighting methods combine with a guided filter to generate a modified guided image filter (MGIF). The function of the MGIF is to split an image into the base layer and the detail layer which are the two elements of an image: illumination and reflection, respectively. The base layer is used to obtain global tone mapping and compress the dynamic range of HDRI while preserving the sharp edges of an object in the HDRI. This has a remarkable effect of reducing halos in the resulting HDRIs. The proposed approach in this article also has several distinct advantages of discriminative operation, tolerance to image size variation, and minimized parameter tuning. According to the experimental results, the proposed method has made progress compared to its existing counterparts when it comes to subjective and quantitative qualities, and color reproduction.

High dynamic range image tone mapping based on asymmetric model of retinal adaptation

Global tone mapping operators using the symmetrical retinal response model to light tend to produce a low dynamic range (LDR) image that exhibits loss of details of its corresponding high dynamic range (HDR) image in a bright or dark area. In this paper, we introduce a new asymmetric sigmoid curve (ASC) based on the model of retinal adaptation encompassing symmetrical S-shaped curve, and present two global tone mapping operators by using the ASC. In the proposed method, an ASC-based tone mapping function is obtained by using a well-known classic photography technique, called zone system. In addition, a contrast-enhancing tone mapping function is introduced by formulating a bi-criteria optimization problem with the luminance histogram of an input HDR image and the ASC-based mapping function. Experimental results demonstrate that the proposed method enhances the global contrast while preserving image details in the tone-mapped LDR image. Moreover, the objective assessment results using an image quality metric indicate that the proposed method shows a high performance to state-of-the-art global tone mapping operators.

Content Aware Image Pre-Compensation.

The goal of image pre-compensation is to process an image such that after being convolved with a known kernel, will appear close to the sharp reference image. In a practical setting, the pre-compensated image has significantly higher dynamic range than the latent image. As a result, some form of tone mapping is needed. In this paper, we show how global tone mapping functions affect contrast and ringing in image pre-compensation. We further enhance contrast and reduce ringing by considering the visual saliency. Specifically, we prioritize contrast preservation in salient regions while tolerating more blurriness elsewhere. For quantitative analysis, we design new metrics to measure the contrast of an image with ringing. Specifically, we set out to find its "equivalent ringing-free" image that matches its intensity histogram and uses its contrast as the measure. We illustrate our approach on projector defocus compensation and visual acuity enhancement. Compared with the state-of-the-art, our approach significantly improves the contrast. We also perform user studies to demonstrate that our method can effectively improve the viewing experience for users with impaired vision.

Gradient-Based Tone Mapping for Rate-Distortion Optimized Backward-Compatible High Dynamic Range Compression.

This paper addresses the problem of designing a global tone mapping operator for rate distortion optimized backward compatible compression of high dynamic range (HDR) images. We address the problem of tone mapping design for two different use cases leading to two different minimization problems. The first problem considered is the minimization of the distortion on the reconstructed HDR signal under a rate constraint on the standard dynamic range (SDR) layer. The second problem remains the same minimization with an additional constraint to preserve a good quality for the SDR signal. Both the distortion and rate are expressed as a function of the spatial gradient in HDR images. Experiments show that the proposed rate and distortion models based on the HDR image gradient accurately predict the real image rate and distortion measures. Experimental results show that for the first minimization, the optimal rate-distortion performances are achieved, and that the second optimization yields the best tradeoff between rate-distortion performance and quality preservation of the SDR signal.

A Novel Tone Mapping Method for Space High Dynamic Range Images

Space high dynamic range imaging is a useful and powerful technique to obtain HDR images. However, HDR images, which carry the whole space scene information, cannot be displayed on most common devices because of the high dynamic range. In this paper, we proposed a novel method that takes advantage of both local and global tone mapping operators. And in the comparison with previous tone mapping operators, we find proposed method is balanced at performance and computational efficiency, which is appropriate for our phonesat with strong computing power.

P‐216L: Late‐News Poster: Reproduction of Perceptual Reality in Standard‐Dynamic‐Range (SDR) Environments Using High‐Dynamic‐Range (HDR) Images Compressed by Global Tone Mapping

We conduct subjective experiments in real HDR environments. The results suggest that (1) most observers could perceive correct luminance correlations by global adaptation rather than local adaptation, but (2) Weber‐ratios were increased. Our solution is to estimate global tone mapping curves in accordance with the above results. The resulting SDR images offer perceptual reality.

HDR tone mapping algorithm based on difference compression with adaptive reference values

Tone mapping remains a challenging problem since tone mapping operators need to produce high perceptual quality under all conditions. In this paper, we propose a new local tone mapping method based on difference compression with adaptive reference values, which can effectively reproduce the details of bright and shadow regions. We also use a global tone mapping method and blend the output images produced by the global and local methods based on objective quality metrics. To quantitatively measure output images, we developed a new objective quality metric for the tone mapped images. The proposed detailness metric measures detail loss in the bright and shadow regions, and shows good correlations with subjective quality. We combined this metric with the recently proposed tone mapped image quality index (TMQI) that may not sufficiently reflect the amount of local detail loss. The experiments show that the proposed algorithm provides better perceptual quality than existing methods.

Analog Current Mode Implementation of Global and Local Tone Mapping Algorithm for WDR Image Display

Analog Current Mode Implementation of Global and Local Tone Mapping Algorithm for WDR Image Display

Fast image enhancement method for display images in ambient light.

Display images under high illumination may appear to have lower lightness and attenuated contrast. In this paper, we review the image degradation phenomenon in ambient light through photoreceptor physiology. The influence of different environmental parameters and their effects on various gray areas in images can be evaluated by the cone response difference. Based on this, we propose a fast global tone mapping method to minimize the cone response difference at different pixel intensity levels of images displayed at different illumination levels. This method has a superior computational efficiency and higher evaluation score than existing algorithms.

Corrected center-surround retinex: application to tone reproduction for high dynamic range images

The tone reproduction is a convenient way of rendering a high dynamic range (HDR) image on the display device with a relatively low dynamic range. This paper proposes an algorithm for tone reproduction based on retinex theory. The proposed model mainly consists of a global adaptation and a local tone reproduction. The global tone mapping is applied to enhance the light variation according to the global ambience of the image. The local tone reproduction is accomplished by the corrected center–surround retinex model. Center–surround retinex theory is corrected with a proposed three-zone histogram theory, which clearly defines the meaning of the reflectance for the local tone reproduction. Next, the illumination map is constructed based on the three-zone histogram theory, while considering the local and global light scattering. Hence, the corrected center–surround retinex consults with not only the surrounding luminance of each pixel, but also the information of the overall histogram. Experimental results show that the proposed algorithm is robust for processing various HDR images and the resulting images are visually pleasing. The merit of the proposed algorithm lies in the balanced presentation of both the whole contrast and details in an image.