Wide2Long: Learning Lens Compression and Perspective Adjustment for Wide-Angle to Telephoto Translation

A translation framework that produces images as if they were captured with a telephoto lens, from images captured with a wide-angle lens, will help in reducing the necessity of complex, expensive and bulky lenses on smartphones. To this end, we propose an image-to-image translation pipeline to simulate the lens compression and perspective adjustment associated with this reconstruction, where the size of the main subject in the images remains the same. We judiciously design depth-based image layering, layer-wise in-painting, redundancy reduction and layer scaling modules to construct the desired tele-photo image, where the pipeline parameters are estimated by a convolutional network. Our approach is compatible with the related optical transformation, and hence, contents behind the main subject are enlarged and that before are diminished, achieving lens compression with appropriate perspective adjustment. Our pipeline performs well qualitatively and quantitatively on several source-target image pairs we have captured solely for this task, and also on images in-the-wild. We show that it can simulate the different amounts of lens compression associated with targeted $2\times$, $4\times$, $8\times$ changes in the focal length. Further, the pipeline is demonstrated to be effective for a sub-class of the lens-compression problem - portrait perspective distortion correction. We also provide an ablation study to show the significance of the various components in the pipeline.