A Study on Improvement in Digital Image Restoration by a Recursive Vector Processing

순환벡터처리에 의한 디지털 영상복원에 관한 연구

This paper discribes technique of the recursive restoration for the images degraded by linear space invariant blur and additive white Gaussian noise. The image is characterized statistically by tis mean and correlation function. An exponential autocorrelation function has been used to model neighborhood model. The vector model was used because of analytical simplicitly and capability to implement brightness correlation function. Base on the vector model, a two-dimensional discrete stochastic a 12 point neighborhood model for represeting images was developme and used the technique of moving window processing to restore blurred and noisy images without dimensionality increesing, It has been shown a 12 point neighborhood model was found to be more adequate than a 8 point pixel model to obtain optimum pixel estimated. If the image is highly correlated, it is necessary to use a large number of points in the neighborhood in order to have improvements in restoring image. It is believed that these result could be applied to a wide range of image processing problem. Because image processing thchniques normally required a 2-D linear filtering.

本論文은 線形空間的不變인 段損(blur)과 自色가우스性雜音에 의해 損傷된 映像에 대한 循環復元(recursive restoration)技法을 論하였다. 映像은 確率設計學的으로 그 平均과 相關函數(correlation function)에 의해 特徵지워진다. 隣接모델(neighborhood model)에 指數的自己相關函數(exponential autocorrelation function)가 사용되며 解析이 간단하고 편리하므로 映像度相關函數를 나타내는데 벡터 모델이 사용된다. 이 벡터 모델을 基本으로 한 映像表現에 있어서 離散的, 統計學的인 12點隣接모델이 開發되고 次元의 增加를 抑制하며 破損되고 雜音섞인 映像을 復元하기 위한 窓(window)移動處理技法이 使用되었다. 12點隣接모델 8點隣接모델보다 優秀한 것으로 나타나며 隣接의 많은 畵素를 요하는 精密畵像에 適合함을 보인다. 이 結果는 線形필터링을 요하는 映像處理에 널리 이용될 수 있음을 나타낸다.