Color Image Enhancement Using a Retinex Algorithm with Bilateral Filtering for Images with Poor Illumination

Mulyantini, Agustien; Choi, Heung‐Kook

doi:10.9717/kmms.2016.19.2.233

Cited by 4 publications

(2 citation statements)

References 23 publications

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“…Earlier retinex methods [2,3,15,16] use Gaussian functions to maintain dynamic range compression and color consistency. Several optimization approaches estimate the illumination map not only using adaptive [17], bilateral [18,19], guided [20], and bright-pass [21] filters but also through derivation [10,12], and structural assumptions [11]. Recently, to address the noise issue within retinex approaches, several methods have applied postprocessing steps such as noise fusion [22] or noise addition [23,24].…”

Section: Plos Onementioning

confidence: 99%

Semi-supervised atmospheric component learning in low-light image problem

Fahim¹,

Saqib²,

Jung³

2023

PLoS ONE

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Ambient lighting conditions play a crucial role in determining the perceptual quality of images from photographic devices. In general, inadequate transmission light and undesired atmospheric conditions jointly degrade the image quality. If we know the desired ambient factors associated with the given low-light image, we can recover the enhanced image easily. Typical deep networks perform enhancement mappings without investigating the light distribution and color formulation properties. This leads to a lack of image instance-adaptive performance in practice. On the other hand, physical model-driven schemes suffer from the need for inherent decompositions and multiple objective minimizations. Moreover, the above approaches are rarely data efficient or free of postprediction tuning. Influenced by the above issues, this study presents a semisupervised training method using no-reference image quality metrics for low-light image restoration. We incorporate the classical haze distribution model to explore the physical properties of the given image to learn the effect of atmospheric components and minimize a single objective for restoration. We validate the performance of our network for six widely used low-light datasets. Experimental studies show that our proposed study achieves a competitive performance for no-reference metrics compared to current state-of-the-art methods. We also show the improved generalization performance of our proposed method which is efficient in preserving face identities in extreme low-light scenarios.

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Section: Plos Onementioning

confidence: 99%

Semi-supervised atmospheric component learning in low-light image problem

Fahim¹,

Saqib²,

Jung³

2023

PLoS ONE

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“…Moreover, Liu et al [143] combined the Retinex algorithm with bilateral filtering, thereby effectively improving the color distortion and detail loss in the final image but also increasing the complexity of the algorithm [144], [145]. Yin et al [146], Mulyantini and Choi [147], Zhang et al [148], Ji et al [149], and Zhang et al [150] proposed Retinex-based algorithms combined with guided filters [151]. Particularly during the early stage of research on Retinex algorithms, scholars obtained many fruitful findings.…”

Section: ) Other Retinex Algorithmsmentioning

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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Contrast improvement on side scan sonar images using retinex based edge preserved technique

Muthuraman¹,

Santhanam²

2022

Mar Geophys Res

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Color Image Enhancement Using a Retinex Algorithm with Bilateral Filtering for Images with Poor Illumination

Cited by 4 publications

References 23 publications

Semi-supervised atmospheric component learning in low-light image problem

Semi-supervised atmospheric component learning in low-light image problem

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

Contrast improvement on side scan sonar images using retinex based edge preserved technique

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