The AI algorithm will reshape the imaging of cameras

The images undergo denoising, enhancement, and other processing to improve image quality, providing a better foundation for subsequent analysis and processing. For example, by using a denoising based on deep learning, it is possible to effectively remove noise from the image while retaining the image's detail information.

Scene recognition: AI algorithms can recognize different scene types, such as landscapes, portraits, architecture, night scenes, etc. According to the scene characteristics, the parameters of the camera are automatically adjusted to obtain the best imaging effect. For example, in night scene mode, the exposure time is automatically increased, the sensitivity is increased and targeted denoising processing is performed.

Automatic focusing and exposure: Real-time monitoring of light changes and object movement, automatically adjusting the camera's focusing exposure settings. For example, when shooting fast-moving objects, the AI focusing system can precisely track the subject, ensuring clear focusing; the automatic exposure function enables the to achieve the best brightness and contrast.

Intelligent white balance: Automatically adjust the white balance according to the shooting environment to ensure that the colors in the photo true and natural, avoiding color cast problems caused by different light sources.

Super-resolution: Using AI algorithms to intellig enlarge low-resolution images into high-resolution images while maintaining details and textures. This breaks through the limitations of traditional image enlargement, allowing us to "mine more visual information from low-quality images.

Detail enhancement: By training on a large amount of image data, the AI model can identify key features in the image perform detail enhancement operations, making the edges of objects in the image clearer and the texture richer.

HDR synthesis: Automatically fuse multiple images with different exposures into high-dynamic-range image, presenting a richer, more realistic color gamut in the picture, and better preserving the details of both the bright and dark parts under contrast lighting conditions.

AI filters: Using machine learning algorithms to automatically endow images with specific artistic styles or visual effects, as converting photos into the painting styles of masters like Picasso and Van Gogh, or achieving a variety of stylized effects such as retro, dreamy, and grain.

Smart effects: Generate effect elements adaptively according to the image content and seamlessly merge them with the original image to produce realistic effects. For example, automatically lifelike landscape images based on the user's semantic annotations.

Face recognition: Quickly lock onto people's faces, precise focusing and recognition, and can also be used for access control systems, security monitoring, etc., to perform corresponding operations based on the recognition results.

Object and tracking: Recognize various objects in the image, such as vehicles, animals, etc., and track the objects of interest, adjusting the shooting angle and length according to the object's movement path and speed.