DifSG2-CCL: Image Reconstruction Based on Special Optical Properties of Water Body

Addressing the unique optical properties of water in underwater images, this letter introduces the DifSG2-CCL model for generating images in complex underwater environments, aiming to mitigate the effects of water quality factors on the generated images. This letter proposes U-CCL (Underwater Cycle Consistency Loss) in the generator loss, allowing the generator to preserve real image information during conversion by reflecting the shot to prevent information loss. Consequently, the generated image is not only more realistic, but also highly consistent with the real image in content. Additionally, this letter utilizes the publicly available 9.235k Sea Anemone Dataset (SA Dataset) with a resolution of $256\times 256$ for training. Experimental results indicate that assigning a weight of 1 to DiffSG2-CCL achieves the best training effect, reducing the FID value to 8.97, while significantly improving the detail and texture of the generated images, approaching aesthetic vision. Thus, this method effectively mitigates the special optical properties of water bodies and offers innovative approaches for generating images in complex underwater environments. The experimental code with pre-trained models will be published shortly at https://github.com/yff0428/DifSG2-CCL/tree/master .