Image super-resolution method using frequency domain features

An image super-resolution method, comprising:

• performing frequency domain transformation on an i-th sample image to obtain an i-th sample spectrogram of the i-th sample image, the i-th sample image being obtained based on an i-th real blur kernel, i being a positive integer;
• inputting the i-th sample spectrogram into a kernel prediction model to obtain an i-th predicted blur kernel outputted by the kernel prediction model, the i-th predicted blur kernel being a convolution kernel;
• obtaining i-th first data based on a combination of the i-th sample spectrogram and the i-th real blur kernel;
• obtaining i-th second data based on a combination of the i-th sample spectrogram and the i-th predicted blur kernel;
• and
• performing iterative training on the kernel prediction model, the iterative training including an i-th-training of the kernel prediction model based on application of a discriminator to the i-th first data and the i-th second data.

An image super-resolution apparatus, comprising:

• processing circuitry configured to
• obtain one or more second images based on a first image, each of the one or more second images having a first size;
• perform frequency domain transformation on the one or more second images to obtain one or more first spectrograms of the one or more second images;
• perform down-sampling processing on each of the one or more first spectrograms to obtain one or more second spectrograms of the one or more second images, the one or more second spectrograms corresponding to a spectral feature of the first image, the spectral feature representing a distribution of a grayscale gradient in the first image, and each of the one or more second spectrograms having a second size less than the first size;
• obtain one or more blur kernels of the one or more second images based on application of a kernel prediction model to each of the one or more second spectrograms of the one or more second images, each of the one or more blur kernels being a convolution kernel;
• generate one or more sub-super-resolved images based on the one or more second images and the one or more blur kernels;
• and
• generate a super-resolved image based on the one or more sub-super-resolved images, a definition of the super-resolved image being higher than a definition of the first image.