Abstract
High-definition display technology for underwater images is of great significance for many applications, such as marine animal observation, seabed mining, and marine fishery production. The traditional underwater visual display systems have problems, such as low visibility, poor real-time performance, and low resolution, and cannot meet the needs of real-time high-definition displays in extreme environments. To solve these issues, we propose an underwater image enhancement method and a corresponding image super-resolution algorithm. To improve the quality of underwater images, we modify the Retinex algorithm and combine it with a neural network. The Retinex algorithm is used to defog the underwater image, and then, the image brightness is improved by applying gamma correction. Then, by combining with the dark channel prior and multilayer perceptron, the transmission map is further refined to improve the dynamic range of the image. In the super-resolution process, the current convolutional neural network reconstruction algorithm is only trained on the Y channel, which will lead to problems due to the insufficient acquisition of the color feature. Therefore, an image super-resolution reconstruction algorithm that is based on color features is proposed. The experimental results show that the proposed method improves the reconstruction effect of the image edges and texture details, increases the image clarity, and enhances the image color recovery.
Highlights
In the process of image formation, due to the influence of severe weather and the limitation of the equipment, the details of the images are often lost in the process of image transmission and storage, which reduces the image resolution
THE PROPOSED METHOD To improve upon the abovementioned method, we propose a multilayer perceptron-based underwater image enhancement method, followed by a color feature-based superresolution method
Superresolution images have a profound importance in the field of marine ecology since studying the ocean is of great significance for disaster prevention, marine resource development and underwater environment monitoring; it is important to research the improvement of underwater images
Summary
In the process of image formation, due to the influence of severe weather and the limitation of the equipment, the details of the images are often lost in the process of image transmission and storage, which reduces the image resolution. Irani and Peleg [19] proposed the iterative back-projection method and used it to solve the problem of superresolution images This method first obtains the initial estimation of the high-resolution image through interpolation. Glasner et al [13] proposed the anchoring field regression (anchored neighborhood regression, A+) method to study a sparse dictionary and return a fixed number of fast superresolution dictionary atoms This method calculates the dictionary in the neighborhood of the atom rather than using a direct calculation in a low resolution image block in the neighborhood, which can reduce the complexity and operation time.
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