Point Spread Functions and Their Applications to Forensic Image Restoration

In the forensic science field, there are many criminal image data including fingerprints, scene photos and surveillance camera videotapes. The evidence may become the key point to solve crimes. However, the evidence may be contaminated by the noise from crime scenes, or distorted by improper collecting procedures. In this case, the evidence will become void and we may lose the chance to arrest the criminals. The field of image restoration began primarily in the space programs of both the United States and the former Soviet Union in the 1950s and early 1960s. The image restoration technology was also extended to other fields, such as medical imaging and film media. Recently, some researchers tried to apply this technology to analyze data from the crime scenes. The ultimate goal of restoration techniques is to improve the quality of a degraded image. The degradation phenomena may come from motion blur, atmospheric turbulence blur, out-of-focus blur and electronic noises. In image restoration processing, we use point spread functions (PSF’s) to model these phenomena and restore degraded images with filters based on those PSF’s. However, in the forensic application, the case-dependent property of the PSF’s makes image restoration processing difficult. In this paper, we review several common PSF’s and apply them to solving image restoration problems. From the experimental results, we can obtain quality-improved images with proper PSF’s and filters.