Scientists have developed a new artificial neural networks powered algorithm that fixes corrupted images by applying a wide range of corrections simultaneously.
University of Bern scientist and colleagues tested their algorithm by taking high-quality, uncorrupted images, purposely introducing severe degradations, then using the algorithm to repair the damage. In many cases, the algorithm outperformed competitors’ techniques, very nearly returning the images to their original state. The team behind the new algorithm explains that because the algorithm can be “trained” to recognize what an ideal, uncorrupted image should look like, it is able to address multiple flaws in a single image.
Artificial neural networks are a type of artificial intelligence algorithm inspired by the structure of the human brain. They can assemble patterns of behavior based on input data, in a process that resembles the way a human brain learns new information. For example, human brains can learn a new language through repeated exposure to words and sentences in specific contexts.
The team behind the new algorithm can “train” their algorithm by exposing it to a large database of high-quality, uncorrupted images widely used for research with artificial neural networks. Because the algorithm can take in a large amount of data and extrapolate the complex parameters that define images–including variations in texture, color, light, shadows and edges–it is able to predict what an ideal, uncorrupted image should look like. Then, it can recognize and fix deviations from these ideal parameters in a new image.
The new algorithm, while powerful, still has room for improvement. Currently, the algorithm works well for fixing easily recognizable “low-level” structures in images, such as sharp edges. The researchers hope to push the algorithm to recognize and repair “high-level” features, including complex textures such as hair and water.