TMO-Net: A Parameter-Free Tone Mapping Operator Using Generative Adversarial Network, and Performance Benchmarking on Large Scale HDR Dataset

Panetta, Karen; Kezebou, Landry; Oludare, Victor; Agaian, Sos S.; Xia, Zehua

doi:10.1109/access.2021.3064295

Cited by 31 publications

(24 citation statements)

References 36 publications

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“…Inspired by the local adaptation mechanism of the biological visual system, some researchers have built models for TM based on the Retinex theory [27,28] or neural circuit in the retina [55]. Recent methods aimed to achieve TM with a deep generative adversarial network have also been reported [30,32,35,41].…”

Section: Related Workmentioning

confidence: 99%

Learning to Adapt to Light

Kaifu¹,

Cheng²,

Zhao³

et al. 2022

Preprint

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Light adaptation or brightness correction is a key step in improving the contrast and visual appeal of an image. There are multiple light-related tasks (for example, low-light enhancement and exposure correction) and previous studies have mainly investigated these tasks individually. However, it is interesting to consider whether these light-related tasks can be executed by a unified model, especially considering that our visual system adapts to external light in such way. In this study, we propose a biologically inspired method to handle light-related image-enhancement tasks with a unified network (called LA-Net). First, a frequency-based decomposition module is designed to decouple the common and characteristic sub-problems of light-related tasks into two pathways. Then, a new module is built inspired by biological visual adaptation to achieve unified light adaptation in the low-frequency pathway. In addition, noise suppression or detail enhancement is achieved effectively in the high-frequency pathway regardless of the light levels. Extensive experiments on three tasks-low-light enhancement, exposure correction, and tone mapping-demonstrate that the proposed method almost obtains state-of-the-art performance compared with recent methods designed for these individual tasks.

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Section: Related Workmentioning

confidence: 99%

Learning to Adapt to Light

Kaifu¹,

Cheng²,

Zhao³

et al. 2022

Preprint

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“…for H and H (k+1) T , respectively, using (26) and (27). Finally, we obtain the TMC T (k+1) (m) for the input pixel value m by…”

Section: E Solution To the Optimizationmentioning

confidence: 99%

“…Rana et al [25] improved the quality of a tone-mapped image by employing a multiscale conditional GAN to alleviate the problems of the conventional GAN-based tone-mapping algorithms [23], [24], such as blurring, tiling patterns, and saturation artifacts. Panetta et al [26] further improved the performance of the GAN-based tone-mapping algorithm by developing an attention-guided generator architecture. Kim et al [27] proposed a detail-preserving tone-mapping algorithm that consists of two CNNs; one restores the details in the input HDR image, and the other compresses the dynamic range.…”

Section: Introductionmentioning

confidence: 99%

Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

Nguyen

Lee

2021

IEEE Access

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High dynamic range (HDR) image and video technology can provide significant picture quality improvement over the standard dynamic range (SDR). However, when HDR content is represented on an SDR display, dynamic range compression may result in image quality deterioration. To address this problem, we propose an optimized human visual system (HVS) response model-based tone-mapping algorithm to preserve the perceptual responses between the HDR image and its tone-mapped image. First, we measure the HVS response differences using a 2D histogram when an HDR image is displayed on an HDR device and when its tone-mapped image is displayed on an SDR device. Then, we formulate an optimization problem to minimize the differences. By efficiently solving the optimization problem, we obtain an optimal tone-mapping curve. Experimental results on actual displays demonstrate that the proposed algorithm provides superior image quality compared with conventional algorithms in terms of both subjective and objective evaluations.INDEX TERMS High dynamic range, HDR10, tone-mapping, human visual system, contrast sensitivity, optimization.

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“…Examples of local TMOs include application of bilateral filtering of the image base layer [12], anisotropic diffusion [13], luminance gradient field manipulation [14], [15], applying the experience of photographic practice [16], relying on the Retinex theory [17]- [19], using localized applications of the Naka-Rushton equation [11]. More recently, deep learning has also been widely applied to the problem of TM [20]- [29].…”

Section: Introductionmentioning

confidence: 99%

HD-RACE: Spray-based Local Tone Mapping Operator

Koščević

Stipetić

Provenzi

et al. 2021

2021 12th International Symposium on Image and Signal Processing and Analysis (ISPA)

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In this paper, a local tone mapping operator is proposed. It is based on the theory of sprays introduced in the Random Sprays Retinex algorithm, a white balance algorithm dealing with the locality of color perception. This tone mapping implementation compresses high dynamic range images by using three types of computations on sprays. These operations are carefully chosen so that the result of their convex combination is a low dynamic image with a high level of detail in all its parts regardless of the original luminance values that may span over large dynamic ranges. Furthermore, a simple local formulation of the Naka-Rushton equation based on random sprays is given. The experimental results are presented and discussed.Index Terms-High dynamic range images, tone mapping, Naka-Rushton equation, Retinex This work was supported by the Croatian Science Foundation under Grant DOK-01-2018. own preference. Additionally, a local formulation of the Naka-Rushton equation based on sprays is proposed. This ensures that all TM calculations of the proposed TMO are entirely performed on local regions represented as sprays.

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TMO-Net: A Parameter-Free Tone Mapping Operator Using Generative Adversarial Network, and Performance Benchmarking on Large Scale HDR Dataset

Cited by 31 publications

References 36 publications

Learning to Adapt to Light

Learning to Adapt to Light

Human Visual System Model-Based Optimized Tone Mapping of High Dynamic Range Images

HD-RACE: Spray-based Local Tone Mapping Operator

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