Abstract
Image super-resolution (SR) reconstruction is to reconstruct a high-resolution (HR) image from one or a series of low-resolution (LR) images in the same scene with a certain amount of prior knowledge. Learning based algorithm is an effective one in image super-resolution reconstruction algorithm. The core idea of the algorithm is to use the training examples of image to increase the high frequency information of the test image to achieve the purpose of image super-resolution reconstruction. This paper presents a novel algorithm for image super resolution based on morphological component analysis (MCA) and dictionary learning. The MCA decomposition based SR algorithm utilizes MCA to decompose an image into the texture part and the structure part and only takes the texture part to train the dictionary. The reconstruction of the texture part is based on sparse representation, while that of the structure part is based on more faster method, the bicubic interpolation. The proposed method improves the robustness of the image, while for different characteristics of textures and structure parts, using a different reconstruction algorithm, better preserves image details, improve the quality of the reconstructed image.
Highlights
With the improvement of living standard, people’s demand for high quality image becomes more and more urgent
This paper presents a novel algorithm for image super resolution based on morphological component analysis (MCA) and dictionary learning
The low resolution training image is interpolated to the same size as the high resolution training image, and the MCA decomposition is carried out to obtain the structure of the interpolated image
Summary
With the improvement of living standard, people’s demand for high quality image becomes more and more urgent. On the basis of Yang proposed a new MCA [7] [8] based and dictionary learning [9] [10] of image super-resolution reconstruction algorithm, first MCA method is decomposed low-resolution image into structure and texture part, and low-resolution texture image is trained the over-complete dictionary. Because of the complexity of texture images, so using super resolution reconstruction method based on sparse representation [11] [12]. In the reduction dimension process using two-dimensional principal component analysis (2DPCA) [13] [14] for dimensionality reduction and using the K-SVD [15] [16] algorithm training the over-complete dictionary to reconstruct the texture image; the structure of the image is relatively flat, using Bicubic interpolation algorithms can better restore the high resolution edge information. The experimental results show that compared with the traditional method and the Yang method, the proposed algorithm improves the convergence speed and the robustness of the image, and improves the quality of the reconstructed image
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