| Summary: | 碩士 === 中央警察大學 === 鑑識科學研究所 === 90 === In the forensic science field, there are many criminal image data including fingerprints, scene photos and surveillance camera videotapes. These evidences may become the key point to solve crime problems. However, the evidence may be contaminated by noise from crime scenes, or distorted by improper collected. 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 the medical imaging and movie media. Recently, some researchers tried to apply this technology to analyze image 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 thesis, we review several common PSF’s and apply them to solving image restoration problems. We also make some discussions on effects (quantization error and truncation error) that make image restoration work difficult.
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