Survey of methods and evaluation of Retinex-inspired image enhancers

Simone, Gabriele; Lecca, Michela; Gianini, G.; Rizzi, Alessandro

doi:10.1117/1.jei.31.6.063055

Cited by 5 publications

(1 citation statement)

References 89 publications

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“…Retinex theory 2 suggests that an image I can be decomposed into an illumination-independent reflectance component IR and an illumination component IL used for structural perception. Recent studies on image enhancement 18 – 22 have demonstrated that enhancing images using the illumination component IL can preserve color information. Similarly, ensuring color constancy involves correcting the reflectance component IR, which is independent of the illumination, making it closer to the reflectance under standard illumination, thus ensuring color constancy in the camera.…”

Section: Introductionmentioning

confidence: 99%

ReCC-GAN: scene global illuminant estimation network based on Retinex and generative adversarial network

Zhao,

Su,

Chai

et al. 2024

J. Electron. Imag.

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In capturing images, cameras often encounter color-cast problems owing to variations in the illumination of different scenes. Existing algorithms attempt to estimate the illuminant information from the raw image data captured by a camera sensor to address color-casting problems. However, these algorithms tend to overlook the potential interference of scene brightness information present in the raw data. Therefore, we introduce a novel illuminant estimation architecture, Retinex color constancy generative adversarial network (ReCC-GAN). ReCC-GAN aims to mitigate the impact of scene brightness information on illuminant estimation tasks, enhance global illuminant estimation accuracy, and resolve color-cast problems. The ReCC-GAN architecture initially separates the reflectance component from the raw data using the Retinex module, reducing the interference caused by the scene brightness information. Subsequently, a specially designed GAN for illuminant estimation was employed to estimate the illuminant vector from this component more effectively. This approach improved illumination estimation accuracy. Experimental results on the Cube++ and NUS-8 dataset demonstrated that the proposed algorithm achieves competitive results compared with other solutions, with outstanding performance on the worst 25% metric. Additionally, tests on various datasets confirmed that the ReCC-GAN exhibited strong generalization performance, further validating its effectiveness and rationality in scene-wide illuminant estimation tasks.

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Section: Introductionmentioning

confidence: 99%

ReCC-GAN: scene global illuminant estimation network based on Retinex and generative adversarial network

Zhao,

Su,

Chai

et al. 2024

J. Electron. Imag.

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Dequantenhancement by spatial color algorithms

Sarti,

Ramella,

Rizzi

2024

J. Opt. Soc. Am. A

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Spatial color algorithms (SCAs) are algorithms grounded in the retinex theory of color sensation that, mimicking the human visual system, perform image enhancement based on the spatial arrangement of the scene. Despite their established role in image enhancement, their potential as dequantizers has never been investigated. Here, we aim to assess the effectiveness of SCAs in addressing the dual objectives of color dequantization and image enhancement at the same time. To this end, we propose the term dequantenhancement. In this paper, through two experiments on a dataset of images, SCAs are evaluated through two distinct pathways: first, quantization followed by filtering to assess both dequantization and enhancement; and second, filtering applied to original images before quantization as further investigation of mainly the dequantization effect. The results are presented both qualitatively, with visual examples, and quantitatively, through metrics including the number of colors, retinal-like subsampling contrast (RSC), and structural similarity index (SSIM).

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Tuning-free and self-supervised image enhancement against ill exposure

Lü¹,

Li²,

Wang

et al. 2023

Opt. Express

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Complex lighting conditions and the limited dynamic range of imaging devices result in captured images with ill exposure and information loss. Existing image enhancement methods based on histogram equalization, Retinex-inspired decomposition model, and deep learning suffer from manual tuning or poor generalization. In this work, we report an image enhancement method against ill exposure with self-supervised learning, enabling tuning-free correction. First, a dual illumination estimation network is constructed to estimate the illumination for under- and over-exposed areas. Thus, we get the corresponding intermediate corrected images. Second, given the intermediate corrected images with different best-exposed areas, Mertens’ multi-exposure fusion strategy is utilized to fuse the intermediate corrected images to acquire a well-exposed image. The correction-fusion manner allows adaptive dealing with various types of ill-exposed images. Finally, the self-supervised learning strategy is studied which learns global histogram adjustment for better generalization. Compared to training on paired datasets, we only need ill-exposed images. This is crucial in cases where paired data is inaccessible or less than perfect. Experiments show that our method can reveal more details with better visual perception than other state-of-the-art methods. Furthermore, the weighted average scores of image naturalness matrics NIQE and BRISQUE, and contrast matrics CEIQ and NSS on five real-world image datasets are boosted by 7%, 15%, 4%, and 2%, respectively, over the recent exposure correction method.

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Survey of methods and evaluation of Retinex-inspired image enhancers

Cited by 5 publications

References 89 publications

ReCC-GAN: scene global illuminant estimation network based on Retinex and generative adversarial network

ReCC-GAN: scene global illuminant estimation network based on Retinex and generative adversarial network

Dequantenhancement by spatial color algorithms

Tuning-free and self-supervised image enhancement against ill exposure

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