Researchers at ETH Zürich have developed an AI-powered system that can turn your measly smartphone snapshots into images that look like they were recorded with a full-blown DSLR… or so they claim.

The project is called ‘DSLR-Quality Photos on Mobile Devices with Deep Convolutional Networks’ and part of the abstract on the project home page reads as follows:

Despite a rapid rise in the quality of built-in smartphone cameras, their physical limitations—small sensor size, compact lenses and the lack of specific hardware—impede them to achieve the quality results of DSLR cameras. In this work we present an end-to-end deep learning approach that bridges this gap by translating ordinary photos into DSLR-quality images.

Of course, the term “DSLR-quality images” could mean many things, but it looks like the software is currently focusing on sharpness, color and tonality. This is in contrast with what smartphone manufacturers tend to refer to as “DSLR-quality images” and what they try to replicate with ‘Portrait’ mode photos: depth-of-field, or rather the lack of it.

To create the software the team started by by training a deep learning system by feeding it photos taken of the same scene using a smartphone camera and a DSLR. This approach worked well but could only improve the quality for the specific smartphone in question. A more sophisticated second version only needs to see two sets of images from different cameras to understand how to apply the image quality from one to the other; in other words: you can feed any photo into the system and apply the image quality of a target camera to it.

The results still need some fine-tuning on occasions—for example, some of the sample shots on display show color casts or a loss of detail after going through the process. However, test images tend to be better exposed and more vibrant. The most obvious improvements can be achieved with smartphone cameras on older or lower-tier devices though.

The scientist hope to eventually use their neural network for modifying the shooting conditions rather than the image quality of the camera. For example, you could turn a photo that was taken on a rainy day into one captured in bright sunshine… for many photographers this might be just a step to far.

If you want to try the current version yourself, you can do so on