Method and terminal for realizing image registering

Abstract

The invention discloses a method and terminal for realizing image registering, and the method comprises the steps of that characteristic points of multiple frames of images are extracted at the same scene at different moments through adoption of a characteristic description operator and an Oriented Brief (ORB) characteristic extraction algorithm; the characteristic points of the extracted frames of images are paired; and multiple frames of images which have completed characteristic points pairing are registered through a preset image registering transformation model. The characteristic points of multiple frames of images at the same scene at different moments are extracted by the ORB characteristic extraction algorithm; the characteristic points of the extracted frames of images are paired; and then the image registering is conducted through the preset image registering transformation model. In this way, the image registering speed and efficiency are increased, and the image quality of image fusion based on the multi-frame fusion noise reduction algorithm is improved.

Description

technical field [0001] The invention relates to image processing technology, in particular to a method and terminal for realizing image registration. Background technique [0002] When shooting an image through a terminal, image imaging is easily disturbed by noise. Taking shooting with a handheld terminal as an example, in order to obtain high-quality imaging effects, it is necessary to perform denoising processing on the image. [0003] At present, commonly used algorithms for denoising processing include single-frame-based linear filtering algorithms and multi-frame fusion noise reduction algorithms; single-frame-based linear filtering algorithms include Gaussian filtering, bilateral filtering, non-local mean filtering, etc. The denoising effect of the linear filtering algorithm is poor; the multi-frame fusion denoising algorithm uses multiple images for image fusion, and the denoising effect is better than the linear filtering algorithm based on a single frame. This type...

AI Technical Summary

Problems solved by technology

[0004] The difficulty of the multi-frame fusion noise reduction algorithm is that the multiple images taken at different times are prone to pixel misalignment. Since the multi-frame fusion noise reduction algorithm performs image fusion at the pixel level, the image registration accuracy needs to be within the unit pixel. Within the accuracy range, if the image registration cann

Images