1. An image processing method that uses a processor to convert an image, the method comprising: performing, on each of multiple first dictionaries produced by dictionary learning, a process to approximate a first image by linear combination of elements of the first dictionary so as to produce a linear combination coefficient and thereby acquiring multiple linear combination coefficients; calculating, by the processor and for each of the multiple linear combination coefficients, a ratio between a largest coefficient element and a second-largest coefficient element among respective coefficient elements of the respective linear combination coefficients, the largest coefficient element having an absolute value that is largest among the respective coefficient elements and the second-largest coefficient element having an absolute value that is second-largest among the respective coefficient elements, thereby obtaining respective ratios of the multiple linear combination coefficients; selecting a specific linear combination coefficient, among the multiple linear combination coefficients, having the ratio that is largest among the respective ratios of the multiple linear combination coefficients; selecting, from multiple second dictionaries corresponding to the first dictionaries, a specific dictionary corresponding to the first dictionary for which the specific linear combination coefficient is selected; and converting the first image into a second image by using linear combination of the specific linear combination coefficient and elements of the specific dictionary.
1. An image processing method that uses a processor to convert an image, the method comprising: performing, on each of multiple first dictionaries produced by dictionary learning, a process to approximate a first image by linear combination of elements of the first dictionary so as to produce a linear combination coefficient and thereby acquiring multiple linear combination coefficients; calculating, by the processor and for each of the multiple linear combination coefficients, a ratio between a largest coefficient element and a second-largest coefficient element among respective coefficient elements of the respective linear combination coefficients, the largest coefficient element having an absolute value that is largest among the respective coefficient elements and the second-largest coefficient element having an absolute value that is second-largest among the respective coefficient elements, thereby obtaining respective ratios of the multiple linear combination coefficients; selecting a specific linear combination coefficient, among the multiple linear combination coefficients, having the ratio that is largest among the respective ratios of the multiple linear combination coefficients; selecting, from multiple second dictionaries corresponding to the first dictionaries, a specific dictionary corresponding to the first dictionary for which the specific linear combination coefficient is selected; and converting the first image into a second image by using linear combination of the specific linear combination coefficient and elements of the specific dictionary. 2. The image processing method according to claim 1 further comprising: providing a first training image relevant to the first image and a second training image relevant to the second image; extracting multiple first training partial images from the first training image; extracting multiple second training partial images from places corresponding to extracted places of the multiple first training partial images in the first training image; subtracting, from pixel values distributed in each of the multiple first and second training partial images, an average value of the pixel values to produce an AC component; converting the AC component of each first training partial image and the AC component of each second training partial image into vectors to produce AC vectors; performing, on each of pairs of the first and second training partial images whose extracted areas correspond to each other, a process to combine the AC vectors of the pair of the first and second training partial images together so as to produce a training vector and thereby producing multiple training vectors; clustering the training vectors to produce multiple clusters each containing the training vectors similar to one another; performing, on each of the multiple clusters, a process to produce a dictionary matrix from the cluster by using a dictionary learning algorithm so as to produce multiple dictionary matrices; and producing the first and second dictionaries from each of the dictionary matrices.