CAJ | 학술논문

考虑到BP神经网络非凡的学习能力和非线性映射能力，提出了利用BP神经网络修复数字图像.由于一般的BP神经网络收敛速度较慢，且易陷入局部极小，产生振荡现象.因此考虑在梯度下降算法的基础上引进动量因子，结果发现收敛速度加快、振荡现象减轻.该方法根据待修复区域的边界寻找相似块，利用相似块周围像素数据得到BP神经网络的权值和阈值.试验表明：文中的方法相对于利用偏微分方程（如BSCB方法）速度要快，而且具有更大的ISNR.
고필도BP신경망락비범적학습능력화비선성영사능력，제출료이용BP신경망락수복수자도상.유우일반적BP신경망락수렴속도교만，차역함입국부겁소，산생진탕현상.인차고필재제도하강산법적기출상인진동량인자，결과발현수렴속도가쾌、진탕현상감경.해방법근거대수복구역적변계심조상사괴，이용상사괴주위상소수거득도BP신경망락적권치화역치.시험표명：문중적방법상대우이용편미분방정（여BSCB방법）속도요쾌，이차구유경대적ISNR.
BP neural network used to repair digital images has be proposed, considering the traits of BP neural network which has an extraordinary capacity for learning and the non-linear mapping. Because ordinary BP neural network converge at a slower rate, fall in to a local minimum easily, and produce oscillatory behavior, introduction of momentum on the basis of the gradient descent algorithm is considered. It get faster convergence speed, smaller oscillation phenomena. Similar blocks will be found in accordance with the border of areas to be restored. With similar block pixel data, BP neural network get its weights and thresholds. The experiment results show this model spend less time than the model utilized partial differential equation (PDE) (for example BSCB), and this model has larger ISNR than PDE model.