Realistic Sonar Image Simulation Using Deep Learning for Underwater Object Detection

Digital Image Art Style Transfer Algorithm and Simulation Based on Deep Learning Model

“…Reference [19] proposed applying the end-to-end network training approach to style migration of videos, which makes it possible to further improve the speed of image style migration while ensuring the stability of video frames. Reference [5] used image style migration to colorize sketches, which can save a large amount of painting coloring time.…”

“…Compared with traditional image style migration methods, the stylized image visuals of deep learning-based image style migration methods have significant advantages in terms of texture and color. Using the deep learning approach, the high-level abstract features of images, such as image texture, image color, and image structure, can be efficiently extracted and combined in a way that is consistent with human visual habits, with excellent versatility and ease of use, eliminating the need for repetitive and tedious mathematical modeling processes [5].…”

Digital Image Art Style Transfer Algorithm and Simulation Based on Deep Learning Model

“…In image style migration, color preservation is a typical use case with high requirements for color processing. Two methods of image style migration based on color preservation are proposed in [20]: one is linear color migration in RGB color space, which migrates the color of the content image into the style image, thus making it possible to maximize the color preservation of the content image during image style migration; the other is image style migration in the luminance space of the content image only, as a way to preserve the original content image color space invariance. [21] used a local linear model to enhance the coordination and correlation between the local and the overall, and realized the way that color migration can refer to multiple images, further enhancing the effect and exibility of image color migration, are shown in Table 1.…”

“…[19] proposed to apply the end-to-end network training approach to style migration of videos, which makes it possible to further improve the speed of image style migration while ensuring the stability of video frames. [20] used image style migration to colorize sketches, which can save a large amount of painting coloring time.…”

“…Compared with traditional image style migration methods, the stylized image visuals of deep learningbased image style migration methods have signi cant advantages in terms of texture and color. Using the deep learning approach, the high-level abstract features of images, such as image texture, image color and image structure, can be e ciently extracted and combined in a way that is consistent with human visual habits, with excellent versatility and ease of use, eliminating the need for repetitive and tedious mathematical modeling processes [5].…”