Image processing method and apparatus, and camera

Abstract

The invention provides an image processing method and apparatus, and a camera. The image processing method comprises the following steps: receiving original image data; executing a first processing operation on the original image data to obtain first image data having a better visual effect than the original image data; executing a second processing operation on the original image data to obtain second image data having more information for video analysis than the first image data; and performing video analysis on the second image data, and superposing a video analysis result on the first image data to obtain third image data. By adoption of the image processing method and apparatus, and the camera provided by the invention, the view experience of users can be taken into account while ensuring the accuracy of algorithm analysis.

Description

technical field [0001] The present invention relates to the technical field of image processing, and more particularly relates to an image processing method and device and a camera. Background technique [0002] Ordinary cameras usually use an imaging module plus a processing unit to complete tasks such as video stream or picture collection, analysis, encoding, storage, and transmission. Binocular cameras usually use two imaging modules plus one or two processing units to implement functions such as measuring object angles and distances on the basis of ordinary cameras. [0003] Wherein, the imaging module generally includes an image sensor and an image signal processor (Image Signal Processor, abbreviated as ISP). The image signal processor is usually used to process the signal output by the front-end image sensor, such as denoising, white balance processing, color space conversion, and processing of sharpness, chroma, and contrast on the raw data collected by the image se...

AI Technical Summary

Problems solved by technology

[0004] However, some of these processes may result in loss of image information or alter the frequency response of the image

For example, reducing noise, increasing sharpness, contrast, etc. can improve the user viewing experience of back-end video or pictures, but at the same time, image details will be lost after denoising; increasing sharpness can increase the sharpness of image edges, making the image Looks sharper, but changes the frequency response of the image

These have no effect on ordinary cameras, but are very unfavorable for algorithm analysis (such as video analysis of images, etc.)

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