Underwater Image Restoration Based on Light Absorption
01 Aug 2018-
TL;DR: A novel modified depth estimation approach for underwater images based on image blurriness as well as light absorption is proposed and evaluated against the state-of-art methods in terms of processing time, PSNR, SSIM etc.
Abstract: Due to the scattered lighting and light absorbed conditions, the underwater images frequently suffered from the low contrast as well as color distortion. As underwater images with the different color tones are captured under different light conditions, it is difficult to perform restoration and enhancement of such images. There are number of conventional methods reported to improve the quality of underwater images, however they constantly performs poor. In this paper, we propose a novel underwater image restoration and enhancement algorithm. We proposed the novel modified depth estimation approach for underwater images based on image blurriness as well as light absorption. To improve the processing speed as well as image quality we proposed novel image blurriness estimation approach in first we performed the image normalization and double precision conversion to improve the processing speed, then apply the edge-stopping pyramid technique rather than Gaussian filtering to improve image quality. The proposed approach is simulated and evaluated against the state-of-art methods in terms of processing time, PSNR, SSIM etc.
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TL;DR: The processed images by the novel color correction method and an enhancement method based on Retinex with dense pixels and adaptive linear histogram transformation for degraded color-biased underwater images have clearer details and uniform visual effect for all channels in RGB color space and can also obtain good performance metrics.
Abstract: 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.
27 citations
Cites background or methods from "Underwater Image Restoration Based ..."
...[56] have good performance, but they may cause color distortion....
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...We compared the proposed method with several novel underwater image enhancement and restoration methods proposed in recent years, such as, global histogram stretching method (GHS) [15], underwater image enhancement by dehazing (UIED) [33], image blurriness and light absorption (UBL) [35], underwater light attenuation prior for underwater image (ULA) [56] global-local networks and compressedhistogram equalization (GLN) [57]....
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...We compared the proposed method with several novel underwater image enhancement and restoration methods proposed in recent years, such as, global histogram stretching method (GHS) [15], underwater image enhancement by dehazing (UIED) [33], image blurriness and light absorption (UBL) [35], underwater light attenuation prior for underwater image (ULA) [56] global-local networks and compressedhistogram equalization (GLN) [57]....
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...(a) raw images; (b) UIED [33]; (c) UBL [35]; (d) ULA [56]; (e) GHS [15]; (f) GLN [57] (g) our method....
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...(a) original color map boards; (b) UIED [33]; (c) UBL [35]; (d) ULA [56]; (e) GHS [15]; (f) GLN [57] (g) our method....
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TL;DR: This work establishes a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference UIQA metrics, and enables ones to evaluate NR UIZA algorithms comprehensively and paves the way for further research onUIQA.
Abstract: Achieving subjective and objective quality assessment of underwater images is of high significance in underwater visual perception and image/video processing. However, the development of underwater image quality assessment (UIQA) is limited for the lack of publicly available underwater image datasets with human subjective scores and reliable objective UIQA metrics. To address this issue, we establish a large-scale underwater image dataset, dubbed UID2021, for evaluating no-reference (NR) UIQA metrics. The constructed dataset contains 60 multiply degraded underwater images collected from various sources, covering six common underwater scenes (i.e., bluish scene, blue-green scene, greenish scene, hazy scene, low-light scene, and turbid scene), and their corresponding 900 quality improved versions are generated by employing 15 state-of-the-art underwater image enhancement and restoration algorithms. Mean opinion scores with 52 observers for each image of UID2021 are also obtained by using the pairwise comparison sorting method. Both in-air and underwater-specific NR IQA algorithms are tested on our constructed dataset to fairly compare their performance and analyze their strengths and weaknesses. Our proposed UID2021 dataset enables ones to evaluate NR UIQA algorithms comprehensively and paves the way for further research on UIQA. The dataset is available at https://github.com/Hou-Guojia/UID2021.
8 citations
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References
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TL;DR: A novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration is proposed.
Abstract: Light scattering and color change are two major sources of distortion for underwater photography. Light scattering is caused by light incident on objects reflected and deflected multiple times by particles present in the water before reaching the camera. This in turn lowers the visibility and contrast of the image captured. Color change corresponds to the varying degrees of attenuation encountered by light traveling in the water with different wavelengths, rendering ambient underwater environments dominated by a bluish tone. No existing underwater processing techniques can handle light scattering and color change distortions suffered by underwater images, and the possible presence of artificial lighting simultaneously. This paper proposes a novel systematic approach to enhance underwater images by a dehazing algorithm, to compensate the attenuation discrepancy along the propagation path, and to take the influence of the possible presence of an artifical light source into consideration. Once the depth map, i.e., distances between the objects and the camera, is estimated, the foreground and background within a scene are segmented. The light intensities of foreground and background are compared to determine whether an artificial light source is employed during the image capturing process. After compensating the effect of artifical light, the haze phenomenon and discrepancy in wavelength attenuation along the underwater propagation path to camera are corrected. Next, the water depth in the image scene is estimated according to the residual energy ratios of different color channels existing in the background light. Based on the amount of attenuation corresponding to each light wavelength, color change compensation is conducted to restore color balance. The performance of the proposed algorithm for wavelength compensation and image dehazing (WCID) is evaluated both objectively and subjectively by utilizing ground-truth color patches and video downloaded from the Youtube website. Both results demonstrate that images with significantly enhanced visibility and superior color fidelity are obtained by the WCID proposed.
782 citations
"Underwater Image Restoration Based ..." refers methods in this paper
...In [2] and [3], the TM was derived by using Dark Channel Prior (DCP), which was focussed to remove haze in images....
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TL;DR: A depth estimation method for underwater scenes based on image blurriness and light absorption is proposed, which can be used in the image formation model (IFM) to restore and enhance underwater images.
Abstract: Underwater images often suffer from color distortion and low contrast, because light is scattered and absorbed when traveling through water. Such images with different color tones can be shot in various lighting conditions, making restoration and enhancement difficult. We propose a depth estimation method for underwater scenes based on image blurriness and light absorption, which can be used in the image formation model (IFM) to restore and enhance underwater images. Previous IFM-based image restoration methods estimate scene depth based on the dark channel prior or the maximum intensity prior. These are frequently invalidated by the lighting conditions in underwater images, leading to poor restoration results. The proposed method estimates underwater scene depth more accurately. Experimental results on restoring real and synthesized underwater images demonstrate that the proposed method outperforms other IFM-based underwater image restoration methods.
433 citations
"Underwater Image Restoration Based ..." refers methods in this paper
...which can be expressed by the equation[1]: ( ) = ( ) ( ) + [1 − ( )] (1)...
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...The transmission map TM( ) is given by the Beer-Lambert law of light attenuation[1]: ( ) = ( ) ....
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...iii) Finally, refine by filling the holes to get a refined blurriness map ( )[1]: ( ) = { [ ( )]} (4) in which is a hole filling reconstruction operator and is a function used for guided filtering....
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...2: Flowchart of the proposed technique[1]...
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02 Dec 2013
TL;DR: This paper proposes a methodology to estimate the transmission in underwater environments which consists on an adaptation of the Dark Channel Prior (DCP), a statistical prior based on properties of images obtained in outdoor natural scenes.
Abstract: This paper proposes a methodology to estimate the transmission in underwater environments which consists on an adaptation of the Dark Channel Prior (DCP), a statistical prior based on properties of images obtained in outdoor natural scenes. Our methodology, called Underwater DCP (UDCP), basically considers that the blue and green color channels are the underwater visual information source, which enables a significant improvement over existing methods based in DCP. This is shown through a comparative study with state of the art techniques, we present a detailed analysis of our technique which shows its applicability and limitations in images acquired from real and simulated scenes.
381 citations
10 Dec 2010
TL;DR: This paper proposes a simple, yet effective, prior that exploits the strong difference in attenuation between the three image color channels in water to estimate the depth of the scene and then uses this estimate to reduce the spatially varying effect of haze in the image.
Abstract: As light is transmitted from subject to observer it is absorbed and scattered by the medium it passes through. In mediums with large suspended particles, such as fog or turbid water, the effect of scattering can drastically decrease the quality of images. In this paper we present an algorithm for removing the effects of light scattering, referred to as dehazing, in underwater images. Our key contribution is to propose a simple, yet effective, prior that exploits the strong difference in attenuation between the three image color channels in water to estimate the depth of the scene. We then use this estimate to reduce the spatially varying effect of haze in the image. Our method works with a single image and does not require any specialized hardware or prior knowledge of the scene. As a by-product of the dehazing process, an up-to-scale depth map of the scene is produced. We present results over multiple real underwater images and over a controlled test set where the target distance and true colors are known.
355 citations
TL;DR: Simulations and image enhancement results show that the proposed method can effectively estimate inherent optical properties of water from the background color of underwater images based on an underwater image formation model and can be used for underwater image enhancement with good performance.
168 citations