A computer-implemented method for increasing image resolution of a digital image performed by one or more processors in communication with a memory, wherein the one or more processors and the memory are part of a system, wherein the method comprises the steps of: • performing (step 702) bicubic upsampling of the digital image to generate a base high-resolution, HR, image; • converting (step 704) the digital image from a red-green-blue, RGB, color space to a Luma, Y, Chroma Blue Difference, Cb, and Chroma Red Difference, Cr, YCbCr color space to generate a low-resolution, LR, residual image; • converting (step 706), using a plurality of convolutional layers of a neural network model, the LR residual image into a plurality of HR residual sub-images corresponding to the digital image, o wherein the neural network model comprises an input layer, and the plurality of convolutional layers comprises four convolutional layers, wherein the input layer is configured to receive the digital image, and an output layer of the four convolutional layers is configured to output the plurality of HR residual sub-images, wherein: ▪ a first layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 8 channels; ▪ a second layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 6 channels; ▪ a third layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 4 channels; and ▪ a fourth layer of the plurality of convolutional layers is configured with 4 channels; and • pixel shifting the plurality of HR residual sub-images to generate an HR residual image; and • generating (step 708) an HR image corresponding to the digital image, using the base HR image and the plurality of HR residual sub-images, wherein the generating of the HR image corresponding to the digital image comprises combining the HR residual image and the base HR image. • performing (step 702) bicubic upsampling of the digital image to generate a base high-resolution, HR, image; • converting (step 704) the digital image from a red-green-blue, RGB, color space to a Luma, Y, Chroma Blue Difference, Cb, and Chroma Red Difference, Cr, YCbCr color space to generate a low-resolution, LR, residual image; • converting (step 706), using a plurality of convolutional layers of a neural network model, the LR residual image into a plurality of HR residual sub-images corresponding to the digital image, o wherein the neural network model comprises an input layer, and the plurality of convolutional layers comprises four convolutional layers, wherein the input layer is configured to receive the digital image, and an output layer of the four convolutional layers is configured to output the plurality of HR residual sub-images, wherein: ▪ a first layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 8 channels; ▪ a second layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 6 channels; ▪ a third layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 4 channels; and ▪ a fourth layer of the plurality of convolutional layers is configured with 4 channels; and o wherein the neural network model comprises an input layer, and the plurality of convolutional layers comprises four convolutional layers, wherein the input layer is configured to receive the digital image, and an output layer of the four convolutional layers is configured to output the plurality of HR residual sub-images, wherein: ▪ a first layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 8 channels; ▪ a second layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 6 channels; ▪ a third layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 4 channels; and ▪ a fourth layer of the plurality of convolutional layers is configured with 4 channels; and ▪ a first layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 8 channels; ▪ a second layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 6 channels; ▪ a third layer of the plurality of convolutional layers is configured with 3 x 3 pixel kernels and 4 channels; and ▪ a fourth layer of the plurality of convolutional layers is configured with 4 channels; and • pixel shifting the plurality of HR residual sub-images to generate an HR residual image; and • generating (step 708) an HR image corresponding to the digital image, using the base HR image and the plurality of HR residual sub-images, wherein the generating of the HR image corresponding to the digital image comprises combining the HR residual image and the base HR image.