Self-adaption online dictionary learning super-resolution method

Abstract

The invention discloses a self-adaption online dictionary learning super-resolution method. The method firstly selects one group of high-resolution training image set and one group of low-resolution training image set,then establishes a relationship on the high-resolution training image set and the low-resolution training image set, and provides more prior information for image reconstructing through high-resolution/low-resolution dictionary to obtain a better reconstructing effect under the condition of high amplification factor, at the same time, the method uses SP (Self Propelled) sparse coding algorithm, overcomes the defect of low reconstructing accuracy due to traditional greedy algorithm and keeps a lower computation complexity. The method has the advantages of overcoming the defects of complex computation and low training speed in the current mainstream dictionary learning algorithms such as MOD (Multimedia On Demand) and K-SVD(k-singular value decomposition) and shortening image reconstructing time in general. Compared with the prior art based on learning image super-resolution, the method of the invention has a higher reconstructing accuracy and a shorter algorithm time.

Description

technical field [0001] The invention relates to image super-resolution technology in the field of signal processing and multimedia data processing, in particular to an image super-resolution method for self-adaptive online dictionary learning. Background technique [0002] Image super-resolution (SR) refers to the process of restoring single or multiple low-resolution images into high-resolution (HR) images. With the increasing application of images in various industries, people have higher and higher requirements for image resolution, because HR images mean that images have high pixel density, which can provide people with more details, and these details are often in the Image plays a key role in practical applications. At present, image super-resolution technology has been widely used in medical imaging, remote sensing detection, public security and other fields. Even in some image application fields, image resolution has become an important indicator to measure image qua...

AI Technical Summary

Problems solved by technology

However, the problem with the algorithm is that it is difficult to establish a neighborhood preserving relationship when the high and low resolution image blocks are embedded in the neighborhood. In order to improve this problem, a training sample selection algorithm based on histogram matching and a method based on local residual embedding have emerged.

However, the algorithm has defects such as too large training samples, too complicated dictionary pair establishment process, and non-adaptive dictionary selection.

In the same year, Pu Jian et al. proposed to use BP sparse coding and K-SVD dictionary update algorithm in dictionary learning, and achieved better super-resolution results than the former; however, this algorithm still has defects such as long training sample time

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