A multi-frame image super-resolution reconstruction method

Abstract

The invention discloses a multi-frame image super-resolution reconstruction method, which introduces the guidance and control kernel regression method into the clustering stage of the adaptive sparse learning model and the regularization reconstruction process, so that the advantages and disadvantages of the two methods complement each other, and the performance is improved. Better reconstruction algorithms. The invention does not need motion registration, so there is no reconstruction error caused by registration error, can significantly improve the quality of reconstructed images, and can be applied to situations involving arbitrary motion patterns. Compared with the existing learning-based multi-frame super-resolution reconstruction method, it provides a simple and efficient integrated reconstruction model that combines the two methods of guided kernel regression and sparse learning, and can simultaneously utilize the global structural self-similarity prior Constraints and sparsity constraints are used to perform regression estimation, so it can better preserve image edge details and reduce image high-frequency distortion. At the same time, the range of motion is not limited, and the application scene is not limited, so it can adapt to complex application 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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