Coordinated underwater dark channel prior for artifact removal of challenging image enhancement

Abstract

When dehazing underwater images, the patch-by-patch dark channel prior (DCP) method is frequently used. After the DCP-based processing, there are still some drawbacks, such as patch artifacts, and these artifacts will seriously affect the subjective quality of some challenging images. To remove the patch artifacts from the DCP-guided enhancement mechanism, this paper proposes a coordinated underwater dark channel prior (CUDCP) method. The proposed method considers the characteristics of the red-green-blue channels with different attenuation situations, and thus the attenuation ratios of the red-green-blue channels are adaptively coordinated in diverse images. The requirement for color restoration is then assessed by an evaluation criterion, and the color restoration is carried out by using the compensated gray world (CGW) theory, which further coordinates the intensity of various red-green-blue channels. Our method next applies a patch-division average filter in accordance with the sub-patch classification. On the typical dataset, the enhanced images of our CUDCP method have higher average underwater image quality measure (UIQM) scores (about 2.274 8) when compared with the original images and those of some state-of-the-art enhancement methods, while the computational cost of CUDCP (about 88.618 8 s) is slightly higher than that of the original DCP (about 87.493 8 s). The experimental results demonstrate that in comparison to state-of-the-art enhancement methods, the proposed method can significantly reduce patch artifacts in challenging image enhancement, while maintaining the objective quality of such underwater images, and also enhancing their subjective quality at a reasonable computational cost.

References

[1] LIC, GUOC, RENW, et al.. An underwater image enhancement benchmark dataset and beyond[J]. IEEE transactions on image processing, 2019, 29: 4376-4389

[2] XUEX, LIZ, MAL, et al.. Investigating intrinsic degradation factors by multi-branch aggregation for real-world underwater image enhancement[J]. Pattern recognition, 2023, 133: 109041

[3] HUH M, ZHANGH, ZHAOZ, et al.. Adaptive single image dehazing using joint local-global illumination adjustment[J]. IEEE transactions on multimedia, 2019, 22(6):1485-1495

[4] GUB, LIW, ZHUM, et al.. Local edge-preserving multiscale decomposition for high dynamic range image tone mapping[J]. IEEE transactions on image processing, 2012, 22(1):70-79

[5] HEK, SUNJ, TANGX. Single image haze removal using dark channel prior[J]. IEEE transactions on pattern analysis and machine intelligence, 2010, 33(12):2341-2353

[6] DREWSP, NASCIMENTOE, MORAESF, et al.. Transmission estimation in underwater single images[C], 2013, New York, IEEE: 825-830

[7] AKKAYNAKD, TREIBITZT. Sea-thru: a method for removing water from underwater images[C], 2019, New York, IEEE: 1682-1691

[8] LIANGZ, WANGY, DINGX, et al.. Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing[J]. Neurocomputing, 2021, 425: 160-172

[9] LIANGZ, DINGX, WANGY, et al.. GUDCP: generalization of underwater dark channel prior for underwater image restoration[J]. IEEE transactions on circuits and systems for video technology, 2022, 32(7):4879-4884

[10] HOUG, LIJ, WANGG, et al.. A novel dark channel prior guided variational framework for underwater image restoration[J]. Journal of visual communication and image representation, 2020, 66: 102732

[11] ANCUTIC O, ANCUTIC, De VLEESCHOUWERC, et al.. Color balance and fusion for underwater image enhancement[J]. IEEE transactions on image processing, 2017, 27(1):379-393

[12] LUJ, LIN, ZHANGS, et al.. Multi-scale adversarial network for underwater image restoration[J]. Optics & laser technology, 2019, 110: 105-113

[13] CARLEVARIS-BIANCON, MOHANA, EUSTICER M. Initial results in underwater single image dehazing[C], 2010, Seattle, WA, USA. New York, IEEE: 1-8

[14] CHIANGJ Y, CHENY C. Underwater image enhancement by wavelength compensation and dehazing[J]. IEEE transactions on image processing, 2011, 21(4):1756-1769

[15] CUIW, LIC, ZHANGC, et al.. Restoration and enhancement of underwater light field image[C], 2018, 451: 93-105

[16] GALDRANA, PARDOD, PICONA, et al.. Automatic red-channel underwater image restoration[J]. Journal of visual communication and image representation, 2015, 26: 132-145

[17] MUSUNURIY R, KWONO S. Haze removal based on refined transmission map for aerial image matching[J]. Applied sciences, 2021, 11(15):6917

[18] SINGHD, KUMARV. Single image haze removal using integrated dark and bright channel prior[J]. Modern physics letters B, 2018, 32(04): 1850051

[19] HANH, QIANF, ZHANGB. Single-image dehazing using scene radiance constraint and color gradient guided filter[J]. Signal, image and video processing, 2022, 16(5): 1297-1304

[20] PORTOM T, BRANZANA A, HOEBERECHTSM. A contrast-guided approach for the enhancement of low-lighting underwater images[J]. Journal of imaging, 2019, 5(10): 79

[21] LI H, LI J, WANG W. A fusion adversarial underwater image enhancement network with a public test dataset[EB/OL]. (2019-06-17) [2022-04-24]. https://arxiv.org/abs/1906.06819.