DoG (Difference of Gaussian) Filter Operator

The DoG (Difference of Gaussian) filter operator is an image processing method commonly employed for edge feature extraction, which can be utilized as preprocessing for segmentation in pattern recognition. By adjusting the variance parameters of two Gaussian functions, different filtering effects can be achieved for various image feature scenarios. The DoG filter operator finds broad applications as it effectively highlights edge information in images while improving image clarity and contrast. Algorithm implementation typically involves convolving the image with two Gaussian kernels having different standard deviations (σ1 and σ2), then computing the pixel-wise difference between the two blurred images. This process amplifies high-frequency components corresponding to edges while suppressing low-frequency background information. Consequently, in the field of image processing, the DoG filter operator serves as a highly valuable tool for feature enhancement and edge detection tasks.