Objective

2

To overcome the low efficiency of dictionary atom screening and the poor effect of image reconstruction results in some super-resolution methods based on sparse representation

which are mostly unconsidered in atom screening

this paper proposes a super-resolution reconstruction algorithm. This algorithm is based on a combination of kernel method and dictionary atomic correlation

which fully uses the correlation between the dictionary and image

and selects the atoms

which significantly contributes to the reconstruction results and improves the efficiency and effect of the reconstruction.

Method

2

First

a set of low-and high-resolution samples are obtained by pre-processing applied on the high-resolution images. Low-and high-resolution dictionaries are learned by using a dictionary learning algorithm. Second

the dictionary atom is uncorrelated to improve the ability of the dictionary atom to express. Third

by using the low-resolution dictionary

the kernel method and dictionary atom screening method are used for sparse representation

to set thresholds to screen for highly correlated atoms

eliminate low-correlation atoms

and then use the normal atoms for normalized processing. The resulting high-and low-resolution dictionary atoms are incoherent

thereby eliminating the similarity between dictionary atoms

enhancing the expressive power of dictionary atoms

and helping to select the next dictionary atoms. In the process of solving the representation coefficient

selecting the appropriate atoms from the low-resolution dictionary to the support set

which is the largest part of the computation

is necessary. When updating the support set

the dictionary of low-resolution images is trained from other images

which leads to the large contribution of some atoms to the samples. The atoms with low correlation often do not contribute during the iteration process

but each iteration has considerable computation costs. At the same time

for the image blocks that need to be restored

a number of highly correlated atomic pairs have a major contribution to reconstruction. To reduce the computational complexity and improve the reconstruction effect

this paper improves the traditional method by using the correlation of the residual and atom to conduct efficient dictionary selection. Finally

the sparse representation problem is solved to obtain sparse coefficients

and the super-resolution image is recovered by using these coefficients. High-resolution image blocks are obtained by using high-resolution dictionary and coefficient representation coefficients

and then

high-resolution images are synthesized by utilizing image blocks.

Result

2

The performance of algorithm reconstruction is measured by PSNR

structural similarity

and time and compared with Yang

MSDSC

and SDCKR algorithms. In the experiment

the following test chart is analyzed in detail

and the ImageNet standard image database is trained to obtain additional detailed experimental results. The experimental results show that

compared with the contrast method

the image reconstruction time is increased by 22.2%

the image structure similarity is increased by 9.06%

and the PSNR is increased by 2.30 dB. The original method based on dictionary learning for dictionary selection has a certain blindness. The atom and reconstruction image correlation degree is low

and the reconstruction effect is poor. This method can reduce the dictionary sparse representation of time consumption and improve the accuracy of sparse representation. In the super-resolution reconstruction of the classical image reconstruction algorithm

the effect is not ideal and the reconstruction time is too long. The main reason is that the dictionary selection efficiency is low

aiming at the abovementioned problem. For the improvement of dictionary learning algorithm in solving the sparse coefficient method in the process of nuclear innovation and the introduction of machine learning and new atom selection method

this paper presents a test with the commissioning of a large number of practical engineering images. The experimental results show that this method can improve the reconstruction effect and reduce the time required for reconstruction.

Conclusion

2

Compared with the same algorithm of dictionary learning

the reconstruction time of this algorithm is also less. Experiments have proven that in this method

the reconstruction time of image sparse representation process is significantly reduced. The reconstruction effect is also improved

with good reconstruction efficiency and effectiveness under the condition of few training samples

which is suitable for practical use.