Abstract
Nowadays, SRR (super resolution image reconstruction) technology is a very effective method in improving spatial resolution of images and obtaining high-definition images. The SRR approach is an image late processing method that does not require any improvement in the hardware of the imaging system. In the SRR reconstruction model, it is the key point of the research to choose a proper cost function to achieve good reconstruction effect. In this paper, based on a lot of research, Lorenzian norm is employed as the error term, Tikhonov regularization is employed as the regularization term in the reconstruction model, and iteration method is employed in the process of SRR. In this way, the outliers and image edge preserving problems in SRR reconstruction process can be effectively solved and a good reconstruction effect can be achieved. A low resolution MRI brain image sequence with motion blur and several noises are used to test the SRR reconstruction algorithm in this paper and the reconstruction results of SRR reconstruction algorithm based on L2 norm are also be used for comparison and analysis. Results from experiments show that the SRR algorithm in this paper has better practicability and effectiveness.
Highlights
In order to achieve a good visual effect, people always want to get clear images with high quality
In order to get HR images, super resolution image reconstruction (SRR) technology has become an effective method in image later processing process
The Lorentzian norm is used as error estimation term, which concentrates the advantages of L1 norm and L2 norm, and can effectively suppress ringing and noise effect, especially salt & pepper noise
Summary
In order to achieve a good visual effect, people always want to get clear images with high quality. In order to get HR images, super resolution image reconstruction (SRR) technology has become an effective method in image later processing process. [6], ML method [7], POCS method [8], mixed-MAP/POCS method [9], and adaptive-filtering method [10], etc Based on these algorithms, combining reconstruction and registration algorithm [11], multi-spectral and color image SRR algorithm [12], compressed sensing reconstruction algorithm [13], and Example-based SRR algorithm have been proposed [14]. The SRR algorithm based on Lorentzian norm [15] is introduced, and is applied to the reconstruction of low resolution MRI brain image sequence. Part 4 gives the reconstruction results of two reconstruction methods based on a series of low resolution MRI brain images.
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