A magnetic resonance imaging = MRI apparatus comprising: an acquisition unit configured to acquire an undersampled k-space data; and a reconstruction unit configured to generate a target image based on the undersampled k-space data, wherein the reconstruction unit comprises: a first sub-reconstruction unit configured to perform initial reconstruction on data corresponding to unsampled positions in the k-space by using a Split Bregman algorithm or approximate sparse coding; a second sub-reconstruction unit configured to decompose the initially reconstructed data in the k-space into multiple frequency bands to thereby generate a plurality of truncated k-spaces, wherein each of the truncated k-spaces corresponds to a respective one of the frequency bands and contains only the frequencies within the respective frequency band, while the other frequencies are filled with zeros, and perform dictionary learning reconstruction on images respectively corresponding to the truncated k-spaces, wherein performing the dictionary learning reconstruction comprises, for each of the images, alternating performance of a sparse approximation and a reconstruction of measured frequencies until convergence, wherein the performance of the sparse approximation comprises: extracting overlapping patches from the image, performing sparse decomposition of the extracted patches into sparse codes by using a dictionary learned in advance based on patches extracted from a set of fully-sampled training MR images, reconstructing the patches as linear combinations of dictionary elements with the obtained sparse codes as coefficients, and generating an intermediate reconstructed image by merging the reconstructed patches, and wherein the reconstruction of the measured frequencies comprises: transforming the intermediate reconstructed image into k-space so as to acquire intermediate k-space data; and reconstructing the measured frequencies in the intermediate k-space data so as to acquire reconstructed k-space data, the measured frequencies being the frequencies sampled in the acquired undersampled k-space data; and an image generator configured to receive reconstructed images respectively corresponding to the multiple frequency bands decomposed by the second sub-reconstruction unit and merge the reconstructed images together to thereby generate a target image. an acquisition unit configured to acquire an undersampled k-space data; and a reconstruction unit configured to generate a target image based on the undersampled k-space data, wherein the reconstruction unit comprises: a first sub-reconstruction unit configured to perform initial reconstruction on data corresponding to unsampled positions in the k-space by using a Split Bregman algorithm or approximate sparse coding; a second sub-reconstruction unit configured to decompose the initially reconstructed data in the k-space into multiple frequency bands to thereby generate a plurality of truncated k-spaces, wherein each of the truncated k-spaces corresponds to a respective one of the frequency bands and contains only the frequencies within the respective frequency band, while the other frequencies are filled with zeros, and perform dictionary learning reconstruction on images respectively corresponding to the truncated k-spaces, wherein performing the dictionary learning reconstruction comprises, for each of the images, alternating performance of a sparse approximation and a reconstruction of measured frequencies until convergence, a first sub-reconstruction unit configured to perform initial reconstruction on data corresponding to unsampled positions in the k-space by using a Split Bregman algorithm or approximate sparse coding; a second sub-reconstruction unit configured to decompose the initially reconstructed data in the k-space into multiple frequency bands to thereby generate a plurality of truncated k-spaces, wherein each of the truncated k-spaces corresponds to a respective one of the frequency bands and contains only the frequencies within the respective frequency band, while the other frequencies are filled with zeros, and perform dictionary learning reconstruction on images respectively corresponding to the truncated k-spaces, wherein performing the dictionary learning reconstruction comprises, for each of the images, alternating performance of a sparse approximation and a reconstruction of measured frequencies until convergence, wherein the performance of the sparse approximation comprises: extracting overlapping patches from the image, performing sparse decomposition of the extracted patches into sparse codes by using a dictionary learned in advance based on patches extracted from a set of fully-sampled training MR images, reconstructing the patches as linear combinations of dictionary elements with the obtained sparse codes as coefficients, and generating an intermediate reconstructed image by merging the reconstructed patches, and wherein the reconstruction of the measured frequencies comprises: transforming the intermediate reconstructed image into k-space so as to acquire intermediate k-space data; and reconstructing the measured frequencies in the intermediate k-space data so as to acquire reconstructed k-space data, the measured frequencies being the frequencies sampled in the acquired undersampled k-space data; and an image generator configured to receive reconstructed images respectively corresponding to the multiple frequency bands decomposed by the second sub-reconstruction unit and merge the reconstructed images together to thereby generate a target image.