Multi-frame image registration and fusion denoising method

Abstract

The invention discloses a multi-frame image registration and fusion denoising method. The method comprises the steps of (1) selecting a reference frame and a to-be-registered frame; (2) registering the to-be-registered frame to the reference frame by utilizing a camera motion model, thereby obtaining a primary registration graph; (3) estimating a motion of a scene target and obtaining motion vector information by utilizing a characteristic graph, and performing registration on the primary registration graph to obtain a secondary registration graph; (4) calculating a consistency pixel graph forthe secondary registration graph; (5) performing pixel domain fusion and transform domain fusion on the secondary registration graph; and (6) performing adaptive weighted fusion on results after thepixel domain fusion and the transform domain fusion to obtain a final denoising result. A motion model of the scene target is estimated by utilizing the characteristic graph, so that the dense motionvector information can be obtained and the problem that a conventional optical flow algorithm is sensitive to noises is improved; and through transform domain fusion and adaptive fusion methods, imagedetails are kept while image noises are removed.

Description

technical field [0001] The invention relates to the field of digital image processing, in particular to a method for registration and fusion of multi-frame images for noise reduction. Background technique [0002] Whether it is for surveillance or mobile phone photography, as an inevitable and very important shooting scene, shooting under low-light conditions has become a difficult problem in obtaining high-quality images because it is difficult to meet the conditions of sufficient light. This is because the image resolution captured by image sensors is generally increasing. For example, the resolution of mobile phone photos is generally in the tens of millions of pixels. However, due to the size limitation of the device itself and design cost considerations, the photosensitive components of the image sensor However, the area cannot increase linearly at the same time, resulting in the reduction of the photosensitive area of ​​a single pixel, so that the contrast of the image...

AI Technical Summary

Problems solved by technology

Feature-based and grid-based image registration methods can only obtain sparse motion field information, and cannot register local moving targets; block-based methods are prone to block effects; existing optical flow methods are sensitive to noise , not suitable for registration of noisy images

In terms of image fusion, image denoising and detail preservation are usually contradictory. The existing fusion methods either retain too much noise in order to maintain image details, or the fusion method is not robust, resulting in blurred image details.

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