A novel optimally gamma corrected intensity span maximization approach for dark image enhancement

Singh, Himanshu; Kumar, Arun; Balyan, L. K.; Singh, Girish Kumar

doi:10.1109/icdsp.2017.8096035

Cited by 25 publications

(4 citation statements)

References 29 publications

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“…The main idea of HE type methods (Acharya & Ray, 2005;Kim, 1997;Pisano et al, 1998;Pizer et al, 1987;Reza, 2004;C.-C. Sun et al, 2005;Zuiderveld, 1994) is to enhance images by mapping the histogram distribution of an image to approximately a uniform one. Different from HE, GC type methods (Guan et al, 2009;Huang et al, 2012;Singh et al, 2017; realize image enhancement by nonlinear editing of its gamma curve, which results in change to the tone of the image. There are also studies that used different value mappings.…”

Section: Value Mapping Methodsmentioning

confidence: 99%

“…In the early years, LIE was achieved by traditional image processing techniques, specifically value mapping methods, such as histogram equalization (HE) (Acharya & Ray, 2005;Kim, 1997;Pisano et al, 1998;Pizer et al, 1987;Reza, 2004;C.-C. Sun, Ruan, Shie, & Pai, 2005;Zuiderveld, 1994) and gamma correction (GC) (Guan, Jian, Hongda, Zhiguo, & Haibin, 2009;Huang, Cheng, & Chiu, 2012;Singh, Kumar, Balyan, & Singh, 2017;W. Wang, Sun, & Ng, 2019).…”

Section: Introductionmentioning

confidence: 99%

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Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment

Liu¹,

Xie²,

Zhao³

et al. 2022

Preprint

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Motivated by their recent advances, deep learning techniques have been widely applied to low-light image enhancement (LIE) problem. Among which, Retinex theory based ones, mostly following a decomposition-adjustment pipeline, have taken an important place due to its physical interpretation and promising performance. However, current investigations on Retinex based deep learning are still not sufficient, ignoring many useful experiences from traditional methods. Besides, the adjustment step is either performed with simple image processing techniques, or by complicated networks, both of which are unsatisfactory in practice. To address these issues, we propose a new deep learning framework for the LIE problem. The proposed framework contains a decomposition network inspired by algorithm unrolling, and adjustment networks considering both global brightness and local brightness sensitivity. By virtue of algorithm unrolling, both implicit priors learned from data and explicit priors borrowed from traditional methods can be embedded in the network, facilitate to better decomposition. Meanwhile, the consideration of global and local brightness can guide designing simple yet effective network modules for adjustment. Besides, to avoid manually parameter tuning, we also propose a self-supervised fine-tuning strategy, which can always guarantee a promising performance. Experiments on a series of typical LIE datasets demonstrated the effectiveness of the proposed method, both quantitatively and visually, as compared with existing methods.

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Section: Value Mapping Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment

Liu¹,

Xie²,

Zhao³

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Low-light Image Enhancement (LIE) is a challenging task as most of the important information in the images is missing. To solve this problem, early approaches usually utilize histogram equalization [1], [2], [3], gamma correction [4], [5], and so on. However, simply adjusting the pixel values does not effectively restore clear images.…”

Section: Introductionmentioning

confidence: 99%

Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement

Pan¹,

Wu²

2022

Preprint

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We propose an effective Structural Prior guided Generative Adversarial Transformer (SPGAT) to solve low-light image enhancement. Our SPGAT mainly contains a generator with two discriminators and a structural prior estimator (SPE). The generator is based on a U-shaped Transformer which is used to explore non-local information for better clear image restoration. The SPE is used to explore useful structures from images to guide the generator for better structural detail estimation. To generate more realistic images, we develop a new structural prior guided adversarial learning method by building the skip connections between the generator and discriminators so that the discriminators can better discriminate between real and fake features. Finally, we propose a parallel windowsbased Swin Transformer block to aggregate different level hierarchical features for high-quality image restoration. Experimental results demonstrate that the proposed SPGAT performs favorably against recent state-of-the-art methods on both synthetic and real-world datasets.

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“…togram equalization [25,4,40] or gamma correction [11,26,32] to enhance low-light images. There are also various classical methods consider Retinex theory [23] and introduce different prior regularized optimization models to characterize the structures of the illumination and reflectance image layers [6,9,13,44].…”

Section: Introductionmentioning

confidence: 99%

Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement

Liu¹,

Ma²,

Zhang³

et al. 2020

Preprint

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Low-light image enhancement plays very important roles in low-level vision field. Recent works have built a large variety of deep learning models to address this task. However, these approaches mostly rely on significant architecture engineering and suffer from high computational burden. In this paper, we propose a new method, named Retinexinspired Unrolling with Architecture Search (RUAS), to construct lightweight yet effective enhancement network for low-light images in real-world scenario. Specifically, building upon Retinex rule, RUAS first establishes models to characterize the intrinsic underexposed structure of lowlight images and unroll their optimization processes to construct our holistic propagation structure. Then by designing a cooperative reference-free learning strategy to discover low-light prior architectures from a compact search space, RUAS is able to obtain a top-performing image enhancement network, which is with fast speed and requires few computational resources. Extensive experiments verify the superiority of our RUAS framework against recently proposed state-of-the-art methods.

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A novel optimally gamma corrected intensity span maximization approach for dark image enhancement

Cited by 25 publications

References 29 publications

Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment

Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment

Structural Prior Guided Generative Adversarial Transformers for Low-Light Image Enhancement

Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancement

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