An image generating method, comprising: receiving a source image including a plurality of images corresponding to separate visual fields; rendering, for display on a 2D display panel as a multi-view glassless 3D image, a two-dimensional, 2D, panel image based on the source image, the 2D panel image corresponding to sub-images, each sub-image comprising pixels of a corresponding visual field among the separate visual fields; and iteratively adjusting pixel values of the 2D panel image based on pixel values of the source image and a predicted 3D image, the predicted 3D image including a plurality of images corresponding to the separate visual fields; and characterized in that the adjusting comprises: generating the predicted 3D image by performing a convolution operation between the sub-images of the 2D panel image and a convolution kernel modelling a view profile and a visibility point spread function of the 2D display panel, wherein the view profile represents the observed brightnesses of pixels of the visual fields when viewed at one of the fields, and the visibility point spread function represents the brightness of an image point in the 2D panel plane as it spreads laterally across the plane of the 2D panel; and adjusting the pixel values of the 2D panel image to reduce the difference between the source image and the predicted 3D image. receiving a source image including a plurality of images corresponding to separate visual fields; rendering, for display on a 2D display panel as a multi-view glassless 3D image, a two-dimensional, 2D, panel image based on the source image, the 2D panel image corresponding to sub-images, each sub-image comprising pixels of a corresponding visual field among the separate visual fields; and iteratively adjusting pixel values of the 2D panel image based on pixel values of the source image and a predicted 3D image, the predicted 3D image including a plurality of images corresponding to the separate visual fields; and characterized in that the adjusting comprises: generating the predicted 3D image by performing a convolution operation between the sub-images of the 2D panel image and a convolution kernel modelling a view profile and a visibility point spread function of the 2D display panel, wherein the view profile represents the observed brightnesses of pixels of the visual fields when viewed at one of the fields, and the visibility point spread function represents the brightness of an image point in the 2D panel plane as it spreads laterally across the plane of the 2D panel; and adjusting the pixel values of the 2D panel image to reduce the difference between the source image and the predicted 3D image. generating the predicted 3D image by performing a convolution operation between the sub-images of the 2D panel image and a convolution kernel modelling a view profile and a visibility point spread function of the 2D display panel, wherein the view profile represents the observed brightnesses of pixels of the visual fields when viewed at one of the fields, and the visibility point spread function represents the brightness of an image point in the 2D panel plane as it spreads laterally across the plane of the 2D panel; and adjusting the pixel values of the 2D panel image to reduce the difference between the source image and the predicted 3D image.