Modified sparse representation based image super-resolution reconstruction method

To improve the geometric structure and texture features of reconstructed images, a novel image super resolution reconstruction (ISR) method based on modified sparse representation, here denoted by MSR_TSR, is discussed in this paper. In this algorithm, edge and texture features of images are synchronously considered, and the over-complete sparse dictionaries of high resolution (HR) and low resolution (LR) image patches, behaving clearer structure features, are learned by feature classification based fast sparse coding (FSC) algorithm. A LR image is first preprocessed by contourlet transform method to denoise unknown noise. Furthermore, four gradient feature images of the LR image preprocessed are extracted. For HR image patches, the edge features are extracted by Canny operator. Then using these edge pixel values as the benchmark to determine whether each image patch's center value is equal to one of edge pixel values, then the edge and texture image patches can be marked out. For gradient image patches, they are first classified by the extreme learning machine (ELM) classifier, thus, corresponding to the class label sequence of LR image patches, the HR image features can also be classified. Furthermore, using FSC algorithm based on the k-means singular value decomposition (K-SVD) model, the edge and texture feature classification dictionaries of HR and LR image patches can be trained. Utilized HR and LR dictionaries trained, a LR image can be reconstructed well. In test, the artificial LR images, namely degraded natural images, are used to testify our ISR method proposed. Utilized the signal noise ratio (SNR) criterion to estimate the quality of reconstructed images and compared with other algorithms of the common K-SVD, FSC and FSC based K-SVD without considering feature classification technique, simulation results show that our method has clear improvement in visual effect and can retain well image edge and texture features.