A method for processing an image, applied to a mobile terminal, the method comprising: acquiring an image to be processed with a first resolution; and processing, by a target neural network model, the image to be processed to obtain a target image, the target image being a denoised image with a second resolution, the second resolution being higher than the first resolution, and the target neural network model comprising a first preset number of convolutional layers and a second preset number of sub-pixel up-sampling modules; wherein the method further comprises: preprocessing noise-free image data to acquire target training data; and training an initial network model by use of the target training data via a stochastic gradient method to acquire the target neural network model, comprising: using the target training data with a preset batch size of each batch as input, and training parameters of the initial network model by use of a preset loss function via the stochastic gradient method, the loss function being a weighted sum of a mean square error, a total variation and perceptual loss, and the mean square error being a weighted sum of a red channel error, a green channel error and a blue channel error. acquiring an image to be processed with a first resolution; and processing, by a target neural network model, the image to be processed to obtain a target image, the target image being a denoised image with a second resolution, the second resolution being higher than the first resolution, and the target neural network model comprising a first preset number of convolutional layers and a second preset number of sub-pixel up-sampling modules; wherein the method further comprises: preprocessing noise-free image data to acquire target training data; and training an initial network model by use of the target training data via a stochastic gradient method to acquire the target neural network model, comprising: using the target training data with a preset batch size of each batch as input, and training parameters of the initial network model by use of a preset loss function via the stochastic gradient method, the loss function being a weighted sum of a mean square error, a total variation and perceptual loss, and the mean square error being a weighted sum of a red channel error, a green channel error and a blue channel error. preprocessing noise-free image data to acquire target training data; and training an initial network model by use of the target training data via a stochastic gradient method to acquire the target neural network model, comprising: using the target training data with a preset batch size of each batch as input, and training parameters of the initial network model by use of a preset loss function via the stochastic gradient method, the loss function being a weighted sum of a mean square error, a total variation and perceptual loss, and the mean square error being a weighted sum of a red channel error, a green channel error and a blue channel error. using the target training data with a preset batch size of each batch as input, and training parameters of the initial network model by use of a preset loss function via the stochastic gradient method, the loss function being a weighted sum of a mean square error, a total variation and perceptual loss, and the mean square error being a weighted sum of a red channel error, a green channel error and a blue channel error.