Learning-based low-bit-rate compression image super-resolution reconstruction method

The invention discloses a learning-based low-bit-rate compression image super-resolution reconstruction method. The learning-based low-bit-rate compression image super-resolution reconstruction method comprises the steps: offline part: firstly compressing low-resolution images by adopting different compression mass parameter values; then filtering the compressed images, taking the quantizing distortion degrees of the filtered compressed images as characteristics, classifying the filtered LR images according to the distortion degrees and establishing a classified sample library, and performing super-resolution model training on all samples respectively. In an online part, the flowing steps are carried out: firstly, filtering input images, then judging a compression distortion class, selecting the sample library and the super-resolution model of the corresponding class according to the judgment result, so as to realize the learning-based super-resolution restoration. Compared with other algorithms, through using the method, the sample library can be self-adaptively regulated so as to match the input LR images according to different distortion degrees, the influence of blocking effect distortion on the super-resolution of the images can be effectively overcome, and compared with the method of directly performing super-resolution restoration on the low-bit-rate distortion images, the images reconstructed by adopting the method has high subjective and objective quality.