Underwater image restoration using color correction and non-local prior

Underwater images often suffer from color distortion and loss of contrast. This is due to the absorption and scattering of light as it travels through water. Although the physical process of underwater imaging is similar to that of haze images in the air. However, traditional dehazing methods cannot produce good results due to the different attenuation of light under different wavelengths in underwater conditions. To overcome this problem, we propose a novel underwater image restoration method based on local depth information priors. First, we use a computer vision-based multi-view geometry method to estimate the local depth information of the image for parameter estimation of the depth compensation model. According to the characteristics of underwater optical imaging, we introduce an underwater color correction method using depth compensation. Second, we propose a method for estimating the global depth image with local depth information priors. Finally, we adopt the global depth image to recover the underwater image. Experimental results demonstrate that the recovered images can achieve better visual quality of underwater images compared to several state-of-the-art methods.

Underwater images often come with blurriness, lack of contrast, and low saturation due to the physics of light propagation, absorption, and scattering in seawater. To improve the visual quality of underwater images, many have proposed image processing methods that vary based on different approaches. We use a generative adversarial network (GAN)-based solution and generate high-quality underwater images equivalent to given raw underwater images by training our network to specify the differences between high-quality and raw underwater images. In our proposed method, which is called dilated GAN (DGAN), we add an additional loss function using structural similarity. Moreover, this method can not only determine the realness of the entire image but also functions with classification ability on each constituent pixel in the discriminator. Finally, using two different datasets, we compare the proposed model with other enhancement methods. We conduct several comparisons and demonstrate via full-reference and nonreference metrics that the proposed approach is able to simultaneously improve clarity and correct color and restores the visual quality of the images acquired in typical underwater scenarios.

Since underwater imaging is affected by the complex water environment, it often leads to severe distortion of the underwater image. To improve the quality of underwater images, underwater image enhancement and restoration methods have been proposed. However, many underwater image enhancement and restoration methods produce over-enhancement or under-enhancement, which affects their application. To better design underwater image enhancement and restoration methods, it is necessary to research the underwater image quality evaluation (UIQE) for underwater image enhancement and restoration methods. Therefore, a subjective evaluation dataset for an underwater image enhancement and restoration method is constructed, and on this basis, an objective quality evaluation method of underwater images, based on the relative symmetry of underwater dark channel prior (UDCP) and the underwater bright channel prior (UBCP) is proposed. Specifically, considering underwater image enhancement in different scenarios, a UIQE dataset is constructed, which contains 405 underwater images, generated from 45 different underwater real images, using 9 representative underwater image enhancement methods. Then, a subjective quality evaluation of the UIQE database is studied. To quantitatively measure the quality of the enhanced and restored underwater images with different characteristics, an objective UIQE index (UIQEI) is used, by extracting and fusing four groups of features, including: (1) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater dark channel map; (2) the joint statistics of normalized gradient magnitude (GM) and Laplacian of Gaussian (LOG) features, based on the underwater bright channel map; (3) the saturation and colorfulness features; (4) the fog density feature; (5) the global contrast feature; these features capture key aspects of underwater images. Finally, the experimental results are analyzed, qualitatively and quantitatively, to illustrate the effectiveness of the proposed UIQEI method.

Captured underwater images usually suffer from severe color cast and low contrast due to wavelength-dependent light absorption and scattering. These degradation issues affect the accuracy of target detection and visual understanding. The underwater image formation model is widely used to improve the visual quality of underwater images. Accurate transmission map and background light estimation are the keys to obtaining clear images. We develop a multi-priors underwater image restoration method with adaptive transmission (MUAT). Concretely, we first propose a calculation method of the dominant channel transmission to cope with pixel interference, which combines two priors of the difference between atmospheric light and pixel values and the difference between the red channel and the blue-green channel. Besides, the attenuation ratio between the superior and inferior channels is adaptively calculated with the background light to solve the color distortion and detail blur caused by the imaging distance. Ultimately, the global white balance method is introduced to solve the color distortion. Experiments on several underwater scene images show that our method obtains accurate transmission and yields better visual results than state-of-the-art methods.

Degradation-aware and color-corrected network for underwater image enhancement

Underwater optical images face various limitations that degrade the image quality compared with optical images taken in our atmosphere. Attenuation according to the wavelength of light and reflection by very small floating objects cause low contrast, blurry clarity, and color degradation in underwater images. We constructed an image data of the Korean sea and enhanced it by learning the characteristics of underwater images using the deep learning techniques of CycleGAN (cycle-consistent adversarial network), UGAN (underwater GAN), FUnIE-GAN (fast underwater image enhancement GAN). In addition, the underwater optical image was enhanced using the image processing technique of Image Fusion. For a quantitative performance comparison, UIQM (underwater image quality measure), which evaluates the performance of the enhancement in terms of colorfulness, sharpness, and contrast, and UCIQE (underwater color image quality evaluation), which evaluates the performance in terms of chroma, luminance, and saturation were calculated. For 100 underwater images taken in Korean seas, the average UIQMs of CycleGAN, UGAN, and FUnIE-GAN were 3.91, 3.42, and 2.66, respectively, and the average UCIQEs were measured to be 29.9, 26.77, and 22.88, respectively. The average UIQM and UCIQE of Image Fusion were 3.63 and 23.59, respectively. CycleGAN and UGAN qualitatively and quantitatively improved the image quality in various underwater environments, and FUnIE-GAN had performance differences depending on the underwater environment. Image Fusion showed good performance in terms of color correction and sharpness enhancement. It is expected that this method can be used for monitoring underwater works and the autonomous operation of unmanned vehicles by improving the visibility of underwater situations more accurately.

Robust vision restoration of underwater images remains a challenge. Owing to the lack of well-matched underwater and in-air images, unsupervised methods based on the cyclic generative adversarial framework have been widely investigated in recent years. However, when using an end-to-end unsupervised approach with only unpaired image data, mode collapse could occur, and the color correction of the restored images is usually poor. In this paper, we propose a data- and physics-driven unsupervised architecture to perform underwater image restoration from unpaired underwater and in-air images. For effective color correction and quality enhancement, an underwater image degeneration model must be explicitly constructed based on the optically unambiguous physics law. Thus, we employ the Jaffe-McGlamery degeneration theory to design a generator and use neural networks to model the process of underwater visual degeneration. Furthermore, we impose physical constraints on the scene depth and degeneration factors for backscattering estimation to avoid the vanishing gradient problem during the training of the hybrid physical-neural model. Experimental results show that the proposed method can be used to perform high-quality restoration of unconstrained underwater images without supervision. On multiple benchmarks, the proposed method outperforms several state-of-the-art supervised and unsupervised approaches. We demonstrate that our method yields encouraging results in real-world applications.

The significant growth in technology advancement leads to underwater videos and images capturing for different purposes. However, the captured underwater images or videos suffer from the low contrast, color distortion, and haziness, thus it is required to enhance the quality of such images or videos for further analysis. The image enhancement techniques already proposed, however such methods are not applicable for the underwater images with the different physical properties. It is challenging research problem to optimize the underwater image quality. In this paper, first attempt towards the restoration of underwater image using the fusion based approach proposed. Previously, the weight maps are computed and fused to enhance the quality of images, but the weight maps introduces the artefacts while performing the fusion, hence to overcome that problem, the optimized fusion technique of weight maps for underwater image enhancement designed. The multi-step fusion approach works independently on two derived images from the original image. Then to optimize the visibility of underwater image, the three weight maps such as saliency, luminance, and chromaticity computed. These weight maps are fused in multi-step manner to overcome the challenge of artefacts and generate the final restored underwater image. The results prove the effectiveness of proposed model compared to single step fusion technique.

Efficient underwater visual environment perception is the key to realizing the autonomous operation of underwater robots. Because of the complex and diverse underwater environment, the underwater images not only have different degrees of color cast but also produce a lot of noise. Due to the existence of noise in the underwater image and the blocking effect in the process of enhancing the image, the enhanced underwater image is still rough. Therefore, an underwater color-cast image enhancement method based on noise suppression and block effect elimination is proposed in this paper. Firstly, an automatic white balance algorithm for brightness and color balance is designed to correct the color deviation of underwater images and effectively restore the brightness and color of underwater images. Secondly, aiming at the problem of a large amount of noise in underwater images, a noise suppression algorithm for heat conduction matrix in the wavelet domain is proposed, which suppresses image noise and improves the contrast and edge detail information of underwater images. Thirdly, for the block effect existing in the process of enhancing the underwater color-cast image, a block effect elimination algorithm based on compressed domain boundary average is proposed, which eliminates the block effect in the enhancement process and balances the bright area and dark area in the image. Lastly, multi-scale image fusion is performed on the images after color correction, noise suppression, and block effect elimination, and finally, the underwater enhanced image with rich features is obtained. The results show that the proposed method is superior to other algorithms in color correction, contrast, and visibility. It also shows that the proposed method corrects the underwater color-cast image to a certain extent and effectively suppresses the noise and block effect of the underwater image, which provides theoretical support for underwater visual environment perception technology.

Multi-scale enhancement fusion for underwater sea cucumber images based on human visual system modelling

Underwater Image Enhancement (UWIE) is essential for improving the quality of Underwater Images (UWIs). However, recent UWIE methods face challenges due to low lighting conditions, contrast issues, color distortion, lower visibility, stability and buoyancy, pressure and temperature, and white balancing problems. Traditional techniques cannot capture the fine changes in UWI texture and cannot learn complex patterns. This study presents a UWIE Network (UWIE-Net) based on a parallel combination of a denoising Deep Convolution Neural Network (DCNN) and blind convolution to improve the overall visual quality of UWIs. The DCNN is used to depict the UWI complex pattern features and focuses on enhancing the image's contrast, color, and texture. Blind convolution is employed in parallel to minimize noise and irregularities in the image texture. Finally, the images obtained at the two parallel layers are fused using wavelet fusion to preserve the edge and texture information of the final enhanced UWI. The effectiveness of UWIE-Net was evaluated on the Underwater Image Enhancement Benchmark Dataset (UIEB), achieving MSE of 23.5, PSNR of 34.42, AG of 13.56, PCQI of 1.23, and UCIQE of 0.83. The UWIE-Net shows notable improvement in the overall visual and structural quality of UWIs compared to existing state-of-the-art methods.

With the increase of spreading distance, the light energy decays rapidly in water. Simultaneously, the light will reflect and deflect multiple times in its travel path. Underwater image typically suffers from poor visibility, low contrast and color distortion due to light scattering and attenuation. In this paper, we put forward a novel algorithm to restore the image based on dark channel prior. The background light is estimated by combining the dark channel prior with saturation map. Then, the transmission maps of different color channels are obtained based on the ratios of attenuation coefficients to recover underwater image. At last, a color correction is applied to balance the shifted color. The experiments show that the method in this paper obviously improves the poor visual quality of underwater images comparing with the existing techniques.

Underwater images are inherently degraded by color distortion, contrast reduction, and uneven brightness, primarily due to light absorption and scattering in water. To mitigate these challenges, a novel enhancement approach is proposed, integrating Local Adaptive Color Correction (LACC) with contrast enhancement based on adaptive Rayleigh distribution stretching and CLAHE (LACC-RCE). Conventional color correction methods predominantly employ global adjustment strategies, which are often inadequate for handling spatially varying color distortions. In contrast, the proposed LACC method incorporates local color analysis, tone-weighted control, and spatially adaptive adjustments, allowing for region-specific color correction. This approach effectively enhances color fidelity and perceptual naturalness, addressing the limitations of global correction techniques. For contrast enhancement, the proposed method leverages the global mapping characteristics of the Rayleigh distribution to improve overall contrast, while CLAHE is employed to adaptively enhance local regions. A weighted fusion strategy is then applied to synthesize high-quality underwater images. Experimental results indicate that LACC-RCE surpasses conventional methods in color restoration, contrast optimization, and detail preservation, thereby enhancing the visual quality of underwater images. This improvement facilitates more reliable inputs for underwater object detection and recognition tasks.

Color correction and restoration based on multi-scale recursive network for underwater optical image

Underwater image restoration is crucial for compute applications and consumer electronics. However, restoring underwater image from a single image is an odd-ill problem due to the complicated underwater environment. To improve the visual quality of underwater image, we propose an underwater image restoration method. First, we present a new underwater image formation model, which takes the properties of underwater imaging and light into account. Then, a medium transmission estimation method for underwater image based on joint prior is proposed, which, respectively, predicts the medium transmissions of three channels of an underwater image. Moreover, we replace the global background light, which is always used in previous underwater image restoration method, with the colors of light source to correct the color casts appeared on the degraded underwater image. The performance of the proposed method is evaluated on the degraded underwater images taken from different scenes by qualitative and quantitative comparisons. Experimental results demonstrate that our results look more visually pleasing and outperforms the results of several existing methods, especial for the colors and contrast.