The Deep-FIR team has recently published a paper on . In we presented a new mechanism that integrates additional information (metadata) describing the degradation process (such as the blur kernel applied, compression level, etc.) to guide the neural network to super-resolve low-resolution images with higher fidelity to the original source. This proposed meta-attention module can be integrated within existing super-resolution techniques to exploit the information available in relevant degradation parameters to improve its performance. This approach was found to improve the performance by 0.3 dB in terms of PSNR. The coding framework used for this paper is available at .