Color Enhancement With Adaptive Illumination Estimation for Low-Backlighted Displays

Pei, Soo‐Chang; Shen, Chih-Tsung

doi:10.1109/tmm.2017.2688924

Cited by 19 publications

(6 citation statements)

References 16 publications

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“…Meanwhile, lighting estimation is also studied as an intermediate result for specific purposes. For example, the lighting is estimated for the purpose of image enhancement [10] and [28]. Han et al present their works to normalize the illumination on human faces, in order to improve the performance of face recognition [16].…”

Section: Related Workmentioning

confidence: 99%

Object-based illumination transferring and rendering for applications of mixed reality

Xu¹,

Cao

2021

Vis Comput

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In applications of augmented reality or mixed reality, rendering virtual objects in real scenes with consistent illumination is crucial for realistic visualization experiences. Prior learning-based methods reported in the literature usually attempt to reconstruct complicated high dynamic range environment maps from limited input, and rely on a separate rendering pipeline to light up the virtual object. In this paper, an object-based illumination transferring and rendering algorithm is proposed to tackle this problem within a unified framework. Given a single low dynamic range image, instead of recovering lighting environment of the entire scene, the proposed algorithm directly infers the relit virtual object. It is achieved by transferring implicit illumination features which are extracted from its nearby planar surfaces. A generative adversarial network is adopted in the proposed algorithm for implicit illumination features extraction and transferring. Compared to previous works in the literature, the proposed algorithm is more robust, as it is able to efficiently recover spatially varying illumination in both indoor and outdoor scene environments. Experiments have been conducted. It is observed that notable experiment results and comparison outcomes have been obtained quantitatively and qualitatively by the proposed algorithm in different environments. It shows the effectiveness and robustness for realistic virtual object insertion and improved realism.

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Section: Related Workmentioning

confidence: 99%

Object-based illumination transferring and rendering for applications of mixed reality

Xu¹,

Cao

2021

Vis Comput

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“…Meanwhile, lighting estimation is also studied as an intermediate result for specific purposes. For example in [10] and [26], authors estimated the lighting for the purpose of image enhancement. While [16] proposed to normalize the illumination on human faces, in order to improve the performance of face recognition.…”

Section: Related Workmentioning

confidence: 99%

IllumiNet: Transferring Illumination from Planar Surfaces to Virtual Objects in Augmented Reality

Xu,

Li,

Zhang

et al. 2020

Preprint

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This paper presents an illumination estimation method for virtual objects in real environment by learning. While previous works tackled this problem by reconstructing high dynamic range (HDR) environment maps or the corresponding spherical harmonics, we do not seek to recover the lighting environment of the entire scene. Given a single RGB image, our method directly infers the relit virtual object by transferring the illumination features extracted from planar surfaces in the scene to the desired geometries. Compared to previous works, our approach is more robust as it works in both indoor and outdoor environments with spatially-varying illumination. Experiments and evaluation results show that our approach outperforms the state-of-the-art quantitatively and qualitatively, achieving realistic augmented experience.

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“…Moreover, as an important branch of research in the field of image processing, low-light image enhancement has interdisciplinary and innovative appeal and broad application prospects and has become a focus of interdisciplinary research in recent years [32]. A large number of researchers at home and abroad have been paying increasing attention to this field for quite some time [33]- [38].…”

Section: Introductionmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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Color Enhancement With Adaptive Illumination Estimation for Low-Backlighted Displays

Cited by 19 publications

References 16 publications

Object-based illumination transferring and rendering for applications of mixed reality

Object-based illumination transferring and rendering for applications of mixed reality

IllumiNet: Transferring Illumination from Planar Surfaces to Virtual Objects in Augmented Reality

An Experiment-Based Review of Low-Light Image Enhancement Methods

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