Super-resolution reconstruction method of multi-frame images

Abstract

The invention discloses a super-resolution reconstruction method of multi-frame images. A steering kernel regression method is introduced separately to a clustering phase of an adaptive sparse learning model and a regularized reconstruction process; the two methods are enabled to complement each other's advantages and disadvantages, forming a reconstruction algorithm of better performance. Motionregistration is not required, and therefore, reconstruction errors due to registration errors are avoided and the quality of a reconstructed image can be significantly improved; the method is applicable to places including any motion mode. Compared with existing learning-based multi-frame super-resolution reconstruction methods, the simple efficient integrated reconstruction model that integratessteering kernel regression and sparse learning is provided which can make use of both global structural self-similar priori constraints and sparse constraints to carry out regressive estimation, so that edge details of an image can be better maintained, and image high-frequency distortion can be relieved; the method is not limited to motion amplitude or application scenes and accordingly is adaptive to complex environments.

Description

technical field [0001] The invention belongs to the technical field of image processing, and relates to a multi-frame image super-resolution reconstruction method, in particular to a multi-frame image super-resolution reconstruction method based on structural clustering and guidance-control kernel regression regularization sparse learning. Background technique [0002] Resolution is one of the important indicators for evaluating image quality. A higher resolution image means that it can provide richer detail information and have better visual effects and image quality. However, in reality, due to the limitation of imaging system hardware conditions, coupled with the influence of factors such as noise and focus deviation, the images we obtain are often of low resolution, which cannot meet the needs of practical applications, and there are phenomena such as noise and blur. Image super-resolution reconstruction can use existing equipment, adopt signal processing technology, and...

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