Underwater image processing is a vast area of research to identify blurred objects in the ocean or sea environments. The light absorbed and scattered by the underwater medium create artifacts in the acquired images due to low-light effects, poor visibility, darkness, illumination variation, and color contrast degradation. In this research work, a novel optical image preprocessing framework was proposed to restore the color contrast of images obtained from various water-colored environments. Domain transform-based multiscale image decomposition method was used to extract the base, residual, and detail layers. Then, the smoothed image was reconstructed from the base layer by applying the adaptive iterative backward-projection algorithm. To obtain dehazed images, a multiscale fusion-based algorithm was developed, which fused the detail layers and reconstructed the images. The outcome of the proposed framework revealed that the contrast degradation and low-light-related artifacts present in the acquired images from various water-colored environments were found to be considerably reduced. The experimental results of the proposed method exhibit scalable improvements over the other methods in terms of visual perception and estimated performance metrics (underwater image quality measure = 6.1186; peak signal-to-noise ratio = 26.68; feature points matching = 96 points; semantic image segmentation accuracy = 0.8465 and processing time of 0.11 seconds on a single image). The novelty of this method relies on the fusion of well-established techniques, which outperformed the advanced techniques like deep learning method in terms of simplicity, speed, performance, datasets, image resolution, and applications.

中文翻译：

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基于多尺度分解和融合的各种水彩环境的色彩对比度恢复

水下图像处理是一个广泛的研究领域，用于识别海洋或海洋环境中的模糊物体。由于低光效应、能见度差、黑暗、照明变化和颜色对比度下降，水下介质吸收和散射的光会在获取的图像中产生伪影。在这项研究工作中，提出了一种新颖的光学图像预处理框架来恢复从各种水彩环境中获得的图像的颜色对比度。基于域变换的多尺度图像分解方法用于提取基础层、残差层和细节层。然后，通过应用自适应迭代反向投影算法从基础层重建平滑图像。为了获得去雾图像，开发了一种基于多尺度融合的算法，它融合了细节层并重建了图像。所提出框架的结果表明，发现从各种水彩环境采集的图像中存在的对比度退化和低光相关伪影显着减少。所提出方法的实验结果在视觉感知和估计性能指标（水下图像质量测量 = 6.1186；峰值信噪比 = 26.68；特征点匹配 = 96 点；语义图像分割精度 = 0.8465，单张图像的处理时间为 0.11 秒）。这种方法的新颖之处在于融合了成熟的技术，在简单性、速度、性能、