Self-adapting regular super resolution image reconstruction method for maintaining edge clear

Abstract

The invention discloses a self-adaptive regularized super-resolution image reconstruction method which can keep marginal definition, which mainly solves the problem that the prior method has edge fog in reconstruction of a degraded image. The method comprises the following steps: an imaging model is constructed; on the basis of an unconstrained objective function constructed by a Lagrangian multiplier method, gradient is increased to approach a bound term; the objective function is expanded; L1 norm is adopted to measure a data approximation term; a self-adaptive bilateral total variation model which can carry out local adaptive control on the smoothing effect is utilized to construct a self-adaptive regular term; a gradient approximation term is added to be as constraint of gradient consistency; edge information is kept; the self-adaptive regular term and a gradient consistency bound term are introduced as constraint conditions; an expanded Lagrangian objective function is constructed and optimized; and an optimized unconstrained objective function is utilized to reconstruct an image, thereby obtaining a high-resolution image of which the edge is kept. The method can keep image edge clear, can inhibit noise and is suitable for restoration treatment on the degraded image.

Description

technical field [0001] The invention belongs to the technical field of signal processing, relates to a super-resolution image reconstruction method, and is applied to restoration of degraded images in multiple fields such as remote sensing, medical imaging, and high-definition television. Background technique [0002] In the process of image acquisition, there are many factors that will lead to the degradation of the image quality, and the ordinary image restoration technology can only restore the frequency of the object to the corresponding cut-off frequency of the diffraction limit, but cannot exceed it. The extra energy and information are helplessly lost. Super-resolution image reconstruction is to try to restore the information outside the cutoff frequency, so that the image can obtain more details and information. Super-resolution reconstruction technology refers to estimating a higher-resolution non-deformed image from some low-resolution deformed images or video seq...

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Problems solved by technology

However, the construction of the regular term of the algorithm does not make full use of the local smoothness characteristics of the image, and the weighting parameters and metric norms are fixed, which fails to achieve local adaptation. Therefore, the adaptive control ability for super-resolution reconstruction is limited. Unable to make a good balance between suppressing noise and reconstructing rich details, blurring the edges of the image and reducing the restoration quality of the image

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