Underwater Single Image Restoration Using Dark Channel Prior

Codevilla, Felipe; Botelho, Silvia Silva da Costa; Drews, Paulo; Filho, Nelson Duarte; Gaya, Joel Felipe de Oliveira

doi:10.1109/navcomp.2014.7

Cited by 16 publications

(3 citation statements)

References 16 publications

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“…The same method has been adapted several times for underwater environments, e.g. [18], [19], [20], [21], and [22]. However, all adaptations lacks to consider the large range of colors that exist underwater by assuming some specific conditions such as the blue channel predominance [18] or the red channel absorption [20,22].…”

Section: Related Workmentioning

confidence: 99%

Single Image Restoration for Participating Media Based on Prior Fusion

Gaya,

Codevilla,

Duarte

et al. 2016

Preprint

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This paper is under consideration at Pattern Recognition Letters. This paper describes a method to restore degraded images captured in a participating media -fog, turbid water, sand storm, etc. Differently from the related work that only deal with a medium, we obtain generality by using an image formation model and a fusion of new image priors. The model considers the image color variation produced by the medium. The proposed restoration method is based on the fusion of these priors and supported by statistics collected on images acquired in both non-participating and participating media. The key of the method is to fuse two complementary measures -local contrast and color data. The obtained results on underwater and foggy images demonstrate the capabilities of the proposed method. Moreover, we evaluated our method using a special dataset for which a ground-truth image is available.

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Section: Related Workmentioning

confidence: 99%

Single Image Restoration for Participating Media Based on Prior Fusion

Gaya,

Codevilla,

Duarte

et al. 2016

Preprint

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“…Serikawa et al [20] proposed a fast joint trigonometric filtering defogging algorithm. Galdran et al [21] made improvements from the point of view of the dark channel prior [22], and proposed a suitable model to highlight the red channel, and some other improved methods based on the dark theory were also proposed in [23][24][25][26][27][28]. Zhao et al [29] combined underwater optical models and the specific properties of background light.…”

Section: Introductionmentioning

confidence: 99%

Color Correction Based on CFA and Enhancement Based on Retinex With Dense Pixels for Underwater Images

Tang

Kwan

et al. 2020

IEEE Access

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Color correction and enhancement for underwater images is challenging due to attenuation and scattering. The underwater images often have low visibility and suffer from color bias. This paper presents a novel color correction method based on color filter array (CFA) and an enhancement method based on Retinex with dense pixels and adaptive linear histogram transformation for degraded color-biased underwater images. For any digital image in the RGB space, which is captured by digital camera with CFA, their RGB values are dependent and coupled because of the interpolation process. So we try to compensate red channel attenuation of underwater degraded images from the green channel and blue channel. Retinex model has been widely used to efficiently handle low brightness and blurred images. The McCann Retinex (MR) method selects a spiral path for pixel comparison to estimate illumination. However, the simple path selection doesn't include global light dark relationship of the whole image. So we design a scheme to gain much well-distributed and denser pixels to obtain more precise intensity of illumination. Besides, we design a piecewise linear function for histogram transform, which is adaptive to the whole RGB value. Experiments on a large number of underwater degraded images show that, the processed images by our method have clearer details and uniform visual effect for all channels in RGB color space and our method can also obtain good performance metrics. INDEX TERMS Underwater image enhancement, underwater image color correction, color filter array (CFA), Retinex, McCann Retinex, adaptive histogram transform.

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“…Finally, authors in [Galdran et al, 2015] and [Codevilla et al, 2014] suggest the use of the inverse of the red channel to achieve a more precise result. This red channel prior (RCP) was suggested because of the fact that in underwater environments red intensity decays faster as distance increases.…”

Section: Dark Channel Prior Dehazingmentioning

confidence: 99%

Visibility in underwater robotics: Benchmarking and single image dehazing

Soler¹

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A mis padres y hermanos. AcknowledgementsThis thesis is the result of more than 5 years of work and it would have been impossible without the support of family, friends and colleagues. May the reader forgive me the discourtesy of following my wish to acknowledge their support in my mother tongue.But before changing to my mother tongue, I want to thank professor Stefan Williams for hosting me at the Australian Centre for Field Robotics in Sydney for four months. Working with his group, and specially with Mitch Bryson and Dushyant Rao, was crucial for the development of the final chapters of this thesis. I also have to acknowledge the ACFR for providing and preparing the datasets used in the deep learning experiments.En primer lugar, me gustaría agradecer a mis directores Raúl Marín y Pedro Sanz la oportunidad de poder trabajar en un campo tan motivante y desafiante como la robótica submarina a través de distintos proyectos en la frontera del conocimiento como Trident, Triton o Merbots. Además de por la ayuda y los consejos desde la experiencia a la hora de introducirme en la investigación.Gracias a la Universitat Jaume I por haber financiado durante 4 años el desarrollo de esta tesis a través del plan de promoción de la investigación (referencia de la ayuda PREDOC/2012/47).También tengo que dar las gracias a todos los miembros del IRSLab de los que he podido aprender, y con los que he colaborado y trabajado como un verdadero equipo durante estos años. A todos quiero dar gracias, Juan Carlos por su disponibilidad a la hora de ayudar, Javi por su espíritu de equipo, Toni por su decisión a la hora de afrontar problemas, David por susánimos y voluntad, Jorge por su insistencia en publicar, José Bernabé por sus consejos y Diego por su entrega.Gracias también a Mario, pese al poco tiempo que coincidimos demostró ser un investigador sobresaliente y un compañero ejemplar que me ayudó en los primeros momentos de este trabajo. Además también tengo que agradecer al RobInLab, especialmente aÁngel, Carlos, Marco, Xavi, Gabriel, Majd y Gustavo, por su ayuda y discusiones en los descansos delante de una taza de café.Durante mi estancia en Sydney tuve la suerte de coincidir con un pequeño grupo de españoles que hizo la estancia mucho más amena. Por ello también quiero darles las gracias, en particular a Guillem y a Raúl con los cuales compartí mucho tiempo dentro y fuera del laboratorio visitando y conociendo Australia, y en general a todas las personas con las que coincidí durante esos meses.Gracias a mi familia y amigos de la Venta del Moro, siempre dispuestos a escuchar después de una larga semana de trabajo. Durante estos años han sido v una distracción necesaria a los problemas del día a día, haciendo el camino de esta tesis mucho más entretenido.Quiero también agradecer a Loli por ser una más de la familia y preocuparse y cuidar de mí día a día desde bien pequeño, ayudándome a convertirme en una persona mejor.Porúltimo, quiero agradecer especialmente la ayuda y el apoyo de mi familia. A mis padres Luis y MaríaÁngeles que m...

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Underwater Single Image Restoration Using Dark Channel Prior

Cited by 16 publications

References 16 publications

Single Image Restoration for Participating Media Based on Prior Fusion

Single Image Restoration for Participating Media Based on Prior Fusion

Color Correction Based on CFA and Enhancement Based on Retinex With Dense Pixels for Underwater Images

Visibility in underwater robotics: Benchmarking and single image dehazing

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