Underwater image enhancement by maximum-likelihood based adaptive color correction and robust scattering removal

Underwater images often exhibit severe color deviations and degraded visibility,which limits many practical applications in ocean engineering.Although extensive research has been conducted into underwater image enhancement,little of which demonstrates the significant robustness and generalization for diverse real-world underwater scenes.In this paper,we propose an adaptive color correction algorithm based on the maximum likelihood estimation of Gaussian parameters,which effectively removes color casts of a variety of underwater images.A novel algorithm using weighted combination of gradient maps in HSV color space and absolute difference of intensity for accurate background light estimation is proposed,which circumvents the influence of white or bright regions that challenges existing physical model-based methods.To enhance contrast of resultant images,a piece-wise affine transform is applied to the transmission map estimated via background light differential.Finally,with the estimated background light and transmission map,the scene radiance is recovered by addressing an inverse problem of image formation model.Extensive experiments reveal that our results are characterized by natural appearance and genuine color,and our method achieves competitive performance with the state-of-the-art methods in terms of objective evaluation metrics,which further validates the better robustness and higher generalization ability of our enhancement model.

The necessity of pretreatment with 0.1%pranoprofen for femtosecond-assisted cataract surgery:A single-center,randomized controlled trial

Purpose:To explore the effect of the variation of pupil diameter(PD)and intraocular pressure(IOP)induced by femtosecond laser treatment on the subsequent phacoemulsfication and intraocular lens implantation.And whether the application of 0.1%pranoprofen could significantly reduce the miosis and increased IOP caused by femtosecond laser treatment in femtosecond laser-assisted cataract surgery(FLACS).Methods:In this study,patients were pretreated with(trial group)or without(control group)topical 0.1%pranoprofen.The PD and IOP were measured at different time points within 30 min after the completion of the femtosecond laser treatment.Results:The comparisons of the two groups showed the PD of patients pretreated with 0.1%pranoprofen was significantly larger than that of the control only at 15 min after FLACS(P=0.046),and there was no significant difference in IOP at any time point(P>0.05).Neither the ratio of significant miosis(PD≤5 mm)nor intraocular hypertension(IOP≥30 mmHg)was significantly different between the control group(1.72%,6.67%)and the trial group(1%,4.17%)(P>0.05).Conclusions:The PD and IOP of patients undergoing FLACS showed fluctuations within a small range.The rates of significant miosis and intraocular hypertension are very low,it is safe for surgeons to complete the follow-up procedures within 30 min after femtosecond laser treatment.Pretreatment with 0.1%pranoprofen exerted a slight,albeit significant prophylactic effect preventing pupil miosis.However,it provided only a limited benefit in patients undergoing FLACS without other complications.

A visual hierarchical framework based model for underwater image enhancement

1 Introduction and main contribution Although there are various computer vision based processing methods for underwater image enhancement, some of them have to be implemented under given priors and the performance of these approaches is still limited. We propose a visual hierarchical framework based model inspired by retinal mechanism, which restores the distortion from color cast and increases contrast simultaneously for underwater images by simulating visual processing of the mammalian retina.

Nighttime image dehazing using color cast removal and dual path multi-scale fusion strategy

Nighttime image dehazing aims to remove the effect of haze on the images captured in nighttime,which however,raises new challenges such as severe color distortion,more complex lighting conditions,and lower contrast.Instead of estimating the transmission map and atmospheric light that are difficult to be accurately acquired in nighttime,we propose a nighttime image dehazing method composed of a color cast removal and a dual path multi-scale fusion algorithm.We first propose a human visual system(HVS)inspired color correction model,which is effective for removing the color deviation on nighttime hazy images.Then,we propose to use dual path strategy that includes an underexposure and a contrast enhancement path for multi-scale fusion,where the weight maps are achieved by selecting appropriate exposed areas under Gaussian pyramids.Extensive experiments demonstrate that the visual effect of the hazy nighttime images in real-world datasets can be significantly improved by our method regarding contrast,color fidelity,and visibility.In addition,our method outperforms the state-of-the-art methods qualitatively and quantitatively.