A computer-implemented video super resolution method comprising: executing recurring instances of a neural network on respective input image frames, said executing comprising: successively executing instances of only a first plurality of layers of a single-image super-resolution component, (SISR) of the neural network for generating a first image (St) at a higher resolution than an input image frame (Xt) based on input image frame information; successively executing a second plurality of layers of a video super-resolution component of the neural network for generating a second image (Vt) at said higher resolution than the input image frame (Xt) based on only: the input image frame information; an output image (Yt-1) at said higher resolution generated by a previous instance of the neural network from a previous input image frame (Xt-1); and intermediate output information (Ht-1) generated by the second plurality of layers of the previous instance; subtracting one of said first image (St) from the other of said second image (Vt) to generate a subtracted image; executing a third plurality of layers on said subtracted image to generate a residual image (Rt); and adding said residual image (Rt) to said first image (St) to produce an output image (Yt) for said instance of said network. executing recurring instances of a neural network on respective input image frames, said executing comprising: successively executing instances of only a first plurality of layers of a single-image super-resolution component, (SISR) of the neural network for generating a first image (St) at a higher resolution than an input image frame (Xt) based on input image frame information; successively executing a second plurality of layers of a video super-resolution component of the neural network for generating a second image (Vt) at said higher resolution than the input image frame (Xt) based on only: the input image frame information; an output image (Yt-1) at said higher resolution generated by a previous instance of the neural network from a previous input image frame (Xt-1); successively executing instances of only a first plurality of layers of a single-image super-resolution component, (SISR) of the neural network for generating a first image (St) at a higher resolution than an input image frame (Xt) based on input image frame information; successively executing a second plurality of layers of a video super-resolution component of the neural network for generating a second image (Vt) at said higher resolution than the input image frame (Xt) based on only: the input image frame information; an output image (Yt-1) at said higher resolution generated by a previous instance of the neural network from a previous input image frame (Xt-1); and intermediate output information (Ht-1) generated by the second plurality of layers of the previous instance; subtracting one of said first image (St) from the other of said second image (Vt) to generate a subtracted image; executing a third plurality of layers on said subtracted image to generate a residual image (Rt); and adding said residual image (Rt) to said first image (St) to produce an output image (Yt) for said instance of said network. subtracting one of said first image (St) from the other of said second image (Vt) to generate a subtracted image; executing a third plurality of layers on said subtracted image to generate a residual image (Rt); and adding said residual image (Rt) to said first image (St) to produce an output image (Yt) for said instance of said network.