A Proposed Workflow for the Restoration of Image Artifacts in Forensic Applications

The last years have witnessed significant developments in image acquisition systems and in algorithms for extracting information from them. Nevertheless, in many scenarios, several factors can hinder the recovery of useful data from images. This is especially true and important in forensic applications, where images are often accidentally captured by an imaging system not engineered for that specific acquisition (for example, the surveillance system designed to monitor the entrance of a bank may accidentally capture the license plate of a vehicle passing outside the bank). Therefore, the acquired images often need to be processed to facilitate extracting information from them. When facing a combination of several impairment factors, such as blur and perspective distortion, several image restoration algorithms must be applied. Then, it is necessary to choose a restoration order, which means the order by which single restoration algorithms are chained together to obtain the enhanced image. This study aims to understand whether such an order may impact the final result. Of course, there exists a wide variety of image impairments; in this study, we focus on the case of an image affected by a combination of optical/motion blur, perspective distortion, and additive noise, which are all widespread artifacts in forensic image applications. To answer the question about the importance of choosing one restoration workflow over another, we first model each considered defect and its restoration operator and then analyze and compare the effects of the composition of such operators on the restored output. Such a comparison is made from both a mathematical and experimental point of view, using both images with synthetically generated impairments and pictures with real degradations. The results show that the restoration order can affect significantly the results, especially when the defects are severe.