When introducing two background optimization methods, the key tools used include image segmentation and image fusion.

1, image segmentation

Image segmentation refers to dividing the image according to different characteristics or attributes, and separating the objects in the image from the background. Commonly used image segmentation methods include threshold segmentation, edge detection, region growing and clustering-based methods.

These methods are helpful to accurately extract the interested object from the image and provide accurate segmentation results for subsequent image processing and synthesis.

2. Image fusion

Image fusion refers to synthesizing multiple images into one image, so that the synthesized images look natural and coherent without obvious traces. In background optimization, image fusion is the key step to synthesize the segmented foreground object with the new background.

Commonly used image fusion methods include pixel-level fusion, weight-based fusion, Laplacian pyramid fusion and deep learning method. These methods can keep the consistency between the foreground object and the new background, and make the synthesized image look natural and complete.

Image segmentation and fusion technology in background optimization and its development trend

Further development of image segmentation.

With the rapid development of computer vision and deep learning technology, important progress has been made in the field of image segmentation. These methods have broad application prospects in medical imaging, autonomous driving, virtual reality and other fields.

The current image segmentation methods can not only accurately extract the object of interest, but also perform more advanced tasks, such as instance segmentation, semantic segmentation and panoramic segmentation.

2. Deep learning technology in image fusion.

Deep learning technology has also made remarkable achievements in the field of image fusion. Using deep neural network can better learn the relationship between two images, so as to achieve a higher quality image fusion effect.

Some methods based on generated antagonistic networks (GAN) perform well in image fusion tasks. There are also some deep learning methods based on feature matching and reconstruction, which have brought new breakthroughs to image fusion technology.