Underwater Image Enhancement Approach Research Articles

An Improved Underwater Image Enhancement Approach for Border Security

An Improved Underwater Image Enhancement Approach for Border Security

Underwater Image Enhancement Based on Color Feature Fusion

The ever-changing underwater environment, coupled with the complex degradation modes of underwater images, poses numerous challenges to underwater image enhancement efforts. Addressing the issues of low contrast and significant color deviations in underwater images, this paper presents an underwater image enhancement approach based on color feature fusion. By leveraging the properties of light propagation underwater, the proposed model employs a multi-channel feature extraction strategy, using convolution blocks of varying sizes to extract features from the red, green, and blue channels, thus effectively learning both global and local information of underwater images. Moreover, an attention mechanism is incorporated to design a residual enhancement module, augmenting the capability of feature representation. Lastly, a dynamic feature enhancement module is designed using deformable convolutions, enabling the network to capture underwater scene information with higher precision. Experimental results on public datasets demonstrate the outstanding performance of our proposed method in underwater image enhancement. Further, object detection experiments conducted on pre- and post-enhanced images underscore the value of our method for downstream tasks.

Underwater image enhancement via red channel maximum attenuation prior and multi-scale detail fusion.

The underwater environment poses great challenges, which have a negative impact on the capture and processing of underwater images. However, currently underwater imaging systems cannot adapt to various underwater environments to guarantee image quality. To address this problem, this paper designs an efficient underwater image enhancement approach that gradually adjusts colors, increases contrast, and enhances details. Based on the red channel maximum attenuation prior, we initially adjust the blue and green channels and correct the red channel from the blue and green channels. Subsequently, the maximum and minimum brightness blocks are estimated in multiple channels to globally stretch the image, which also includes our improved guided noise reduction filtering. Finally, in order to amplify local details without affecting the naturalness of the results, we use a pyramid fusion model to fuse local details extracted from two methods, taking into account the detail restoration effect of the optical model. The enhanced underwater image through our method has rich colors without distortion, effectively improved contrast and details. The objective and subjective evaluations indicate that our approach surpasses the state-of-the-art methods currently. Furthermore, our approach is versatile and can be applied to diverse underwater scenes, which facilitates subsequent applications.

Twice Mixing: A rank learning based quality assessment approach for underwater image enhancement

Objectively and accurately evaluating underwater images generated by different enhancement algorithms is an essential issue, which however is still largely under-explored. In this paper, we present a novel rank learning guided no-reference quality assessment method to evaluate different underwater image enhancement (UIE) algorithms. It is also the first work that utilizes deep learning approaches to address this problem. Our approach, termed Twice Mixing, is motivated by the observation that a mid-quality image can be generated by mixing a high-quality image and its low-quality version. Twice Mixing is trained based on an elaborately formulated self-supervision mechanism. Specifically, before each iteration, we randomly generate two mixing ratios which will be utilized for both generating virtual images and guiding the network training. In the test phase, a single branch of the network is extracted to predict the quality rankings of different UIE outputs. Additionally, to train our network, we construct a new dataset that contains over 2200 raw underwater images and their high/low-quality versions. Twice Mixing is evaluated on both synthetic and real-world datasets. Experimental results show that the proposed approach outperforms the previous methods significantly.

The Retinex based improved underwater image enhancement

The underwater images suffer from low contrast and color distortion due to variable attenuation of light and nonuniform absorption of red, green and blue components. In this paper, we propose a Retinex-based underwater image enhancement approach. First, we perform underwater image enhancement using the contrast limited adaptive histogram equalization (CLAHE), which limits the noise and enhances the contrast of the dark components of the underwater image at the cost of blurring the visual information. Then, in order to restore the distorted colors, we perform the Retinex-based enhancement of the CLAHE processed image. Next, in order to restore the distorted edges and achieve smoothing of the blurred parts of image, we perform bilateral filtering on the Retinex processed image. In order to utilize the individual strengths of CLAHE, Retinex and bilateral filtering algorithms in a single framework, we determine the suitable parameter values. The qualitative and quantitative performance comparison with some of the existing approaches shows that the proposed approach achieves better enhancement of the underwater images.

An approach for underwater image enhancement based on color correction and dehazing

Due to the absorption and scattering effect on light when traveling in water, underwater images exhibit serious weakening such as color deviation, low contrast, and blurry details. Traditional algorithms have certain limitations in the case of these images with varying degrees of fuzziness and color deviation. To address these problems, a new approach for single underwater image enhancement based on fusion technology was proposed in this article. First, the original image is preprocessed by the white balance algorithm and dark channel prior dehazing technologies, respectively; then two input images were obtained by color correction and contrast enhancement; and finally, the enhanced image was obtained by utilizing the multiscale fusion strategy which is based on the weighted maps constructed by combining the features of global contrast, local contrast, saliency, and exposedness. Qualitative results revealed that the proposed approach significantly removed haze, corrected color deviation, and preserved image naturalness. For quantitative results, the test with 400 underwater images showed that the proposed approach produced a lower average value of mean square error and a higher average value of peak signal-to-noise ratio than the compared method. Moreover, the enhanced results obtain the highest average value in terms of underwater image quality measures among the comparable methods, illustrating that our approach achieves superior performance on different levels of distorted and hazy images.

Real-time underwater image enhancement: An improved approach for imaging with AUV-150

An RGB YCbCr Processing method (RYPro) is proposed for underwater images commonly suffering from low contrast and poor color quality. The degradation in image quality may be attributed to absorption and backscattering of light by suspended underwater particles. Moreover, as the depth increases, different colors are absorbed by the surrounding medium depending on the wavelengths. In particular, blue/green color is dominant in the underwater ambience which is known as color cast. For further processing of the image, enhancement remains an essential preprocessing operation. Color equalization is a widely adopted approach for underwater image enhancement. Traditional methods normally involve blind color equalization for enhancing the image under test. In the present work, processing sequence of the proposed method includes noise removal using linear and non-linear filters followed by adaptive contrast correction in the RGB and YCbCr color planes. Performance of the proposed method is evaluated and compared with three golden methods, namely, Gray World (GW), White Patch (WP), Adobe Photoshop Equalization (APE) and a recently developed method entitled “Unsupervised Color Correction Method (UCM)”. In view of its simplicity and computational ease, the proposed method is recommended for real-time applications. Suitability of the proposed method is validated by real-time implementation during the testing of the Autonomous Underwater Vehicle (AUV-150) developed indigenously by CSIR-CMERI.

Underwater Color Image Enhancement Using Combining Schemes

AbstractUnderwater color image processing has received considerable attention in the last few decades for underwater image-based observation. In this article, a novel underwater image enhancement approach using combining schemes is presented. This study aims to improve color correction under nonuniform illumination conditions. The objective of this approach is threefold. First, to correct nonuniform illumination and enhance contrast in the image, homomorphic filtering is used. Second, the color contrast of an image is equalized by a contrast stretching algorithm in RGB (red, green and blue) color space. Finally, the noise amplified after the previous two steps is suppressed by using wavelet domain denoising based on threshold processing. The comparison of experimental results shows that the proposed approach of underwater image enhancement can correct the color imbalance and is especially suitable for processing underwater color images that have nonuniform lighting.