小波阈值降噪在手写数字识别图像预处理中的应用

Application of Wavelet Threshold Denoising in Image Preprocessing of Handwritten Numeral Recognition

摘要: 图像降噪是手写数字识别图像预处理中的重要一环。针对传统降噪方法的不足，本文提出了一种基于改进半软阈值函数的小波阈值降噪方法。该方法一方面可以有效减弱硬阈值函数的自激振荡和软阈值函数的模糊失真，另一方面与传统的半软阈值函数相比，在阈值附近过渡更加平滑。结合涡流搜索算法和广义交叉验证准则，实现了分层阈值的自适应确定。仿真结果验证了该方法的可行性和有效性。相比传统的小波阈值降噪方法，改进后的方法能够显著提高手写数字识别的正确率。

Abstract: Image denoising is an important part of image preprocessing of handwritten numeral recognition. For the shortcomings of traditional denoising methods, a wavelet threshold denoising method based on improved semisoft threshold function is proposed. On the one hand, this method can effectively reduce the self-oscillation of hard threshold function and the fuzzy distortion of soft threshold function. On the other hand, compared with the traditional semisoft threshold function, the transition near the threshold is smoother. Combining the vortex search algorithm and the generalized cross validation criterion, the hierarchical threshold is determined adaptively. Simulation results verify the feasibility and effectiveness of this method. Compared with the traditional wavelet threshold denoising methods, the improved method can significantly increase the accuracy of handwritten numeral recognition.