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...

AI Technical Summary

Problems solved by technology

However, motion registration often has a huge amount of calculation, and it will inevitably bring errors. Registration errors will be converted into reconstruction errors, which will restrict the quality of reconstructed images. Especially when the image quality is low, the registration errors will be large and the reconstruction effect will be poor.

On the other hand, the existing registration methods assume that the inter-frame transformation is a certain type of motion mode, that is, a certain spatial transformation model is assumed, while the actual video sequence images may contain arbitrary motion modes

[0005] Chinese invention patent "A method and system for multi-frame super-resolution image reconstruction" (201610491560.0) includes the use of SIFT features for image registration, which belongs to the traditional multi-frame super-resolution method, and its disadvantage is: SIFT feature registration can only Dealing with the case where the target is an affine transformation (translation + rotation + scaling) between different frames, it cannot be applied to situations other than the three transformation relationships including translation, rotation, and scaling. The application scenarios are limited; and there must be errors in registration, which will Bring errors to the subsequent reconstruction, causing distortion of the reconstructed image

Chinese invention patent "A multi-frame image super-resolution reconstruction method and its reconstruction system" (201610049469.3) includes the use of interim results obtained from geometric transformations and filter transfer functions obtained from blur kernels to construct energy for super-resolution reconstruction function, using the graph cut algorithm to minimize the solution, the final high-resolution image can be obtained, and the reconstruction effect and reconstruction speed are improved. The disadvantage is that this method assumes that there is a geometric transformation relationship between different frames. The transformation relationship needs to be known through registration, the registration error will limit the quality of the reconstructed image, and the application scenarios are limited

Its shortcoming is that it only considers the similarity redundant information within a certain range for estimation and reconstruction, and it is not based on global information estimation. When the motion between frames is large and exceeds the search range, the quality of the reconstructed image will be significantly reduced. ; and the prior constraints are single, the reconstruction effect is limited

Its shortcomings are: the two reconstruction processes of the two constraints are simply superimposed, and are not integrated into a more effective integrated reconstruction model; and when the first reconstruction is performed, only the similarity redundant information within a certain range is considered. , is not estimated based on global information, so the range of motion is limited

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