A computer-implemented method for reconstructing a super resolution, SR, image from multispectral, MS, images, comprising: accepting data including low resolution multispectral, LRMS, images and high resolution panchromatic, HRPAN, images of a scene, each LRMS image is associated with a HRPAN image, and storing the data in a memory; iteratively, fusing a LRMS image with an associated HRPAN image of the scene using a trained fusing algorithm, by a processor, each iteration includes: using a gradient descent, GD, approach, to generate an intermediate high-resolution multispectral, IHRMS image with a decreased error indicated in an objective cost function; projecting the IHRMS image using a convolutional neural network, CNN, to obtain an estimated synthesized high-resolution multispectral, ESHRMS, image; using the ESHRMS image, along with the HRPAN image, as an input to the GD approach, to obtain an updated IHRMS image for each iteration, which is used as an input to another CNN, to obtain an updated ESHRMS image; and generating a fused high-spatial and high-spectral resolution MS image upon processing all of the accepted LRMS images and HRPAN images of the scene, ending the iterations and completing an iteration cycle; and outputting, via an output interface, the fused high-spatial and high-spectral resolution MS image to a communication network or to a display device. accepting data including low resolution multispectral, LRMS, images and high resolution panchromatic, HRPAN, images of a scene, each LRMS image is associated with a HRPAN image, and storing the data in a memory; iteratively, fusing a LRMS image with an associated HRPAN image of the scene using a trained fusing algorithm, by a processor, each iteration includes: using a gradient descent, GD, approach, to generate an intermediate high-resolution multispectral, IHRMS image with a decreased error indicated in an objective cost function; projecting the IHRMS image using a convolutional neural network, CNN, to obtain an estimated synthesized high-resolution multispectral, ESHRMS, image; using the ESHRMS image, along with the HRPAN image, as an input to the GD approach, to obtain an updated IHRMS image for each iteration, which is used as an input to another CNN, to obtain an updated ESHRMS image; and generating a fused high-spatial and high-spectral resolution MS image upon processing all of the accepted LRMS images and HRPAN images of the scene, ending the iterations and completing an iteration cycle; and using a gradient descent, GD, approach, to generate an intermediate high-resolution multispectral, IHRMS image with a decreased error indicated in an objective cost function; projecting the IHRMS image using a convolutional neural network, CNN, to obtain an estimated synthesized high-resolution multispectral, ESHRMS, image; using the ESHRMS image, along with the HRPAN image, as an input to the GD approach, to obtain an updated IHRMS image for each iteration, which is used as an input to another CNN, to obtain an updated ESHRMS image; and generating a fused high-spatial and high-spectral resolution MS image upon processing all of the accepted LRMS images and HRPAN images of the scene, ending the iterations and completing an iteration cycle; and outputting, via an output interface, the fused high-spatial and high-spectral resolution MS image to a communication network or to a display device.