diff --git a/opcomplex.cpp b/opcomplex.cpp
new file mode 100644
index 0000000..6a7019a
--- /dev/null
+++ b/opcomplex.cpp
@@ -0,0 +1,140 @@
+#include <iostream>
+#include<arm_neon.h>
+#include <time.h>
+
+#define IDx(n) ((n) % 3)
+
+#define H 600
+#define W 600
+
+
+void applyGaussianBlur(float src[][W], float dst[][W], int h, int w, float kernel[3][3]) {
+    for (int i = 1; i < h -1;++i) {
+        for (int j = 1; j < w - 1; ++j) {
+            dst[i][j] =src[i - 1][j - 1] * kernel[0][0] + src[i - 1][j] * kernel[0][1] + src[i - 1][j + 1] * kernel[0][2] +
+                        src[i][j - 1] * kernel[1][0] + src[i][j] * kernel[1][1] + src[i][j + 1] * kernel[1][2] +
+                        src[i + 1][j - 1] * kernel[2][0] + src[i + 1][j] * kernel[2][1] + src[i + 1][j + 1] * kernel[2][2];
+        }
+    }
+}
+
+void applySeparableGaussianBlur(float src[][W], float dst[][W], int h, int w, float kx[3], float ky[3]) {
+    float buf[3][W+3];
+
+    for (int i = 0; i < 2; ++i) {
+        for (int j = 1; j< w - 1; ++j) {
+            buf[i][j] = src[i][j - 1] * kx[0] + src[i][j] * kx[1] + src[i][j + 1] * kx[2];
+        }
+    }
+
+    for (int i = 1; i < h - 1; ++i) {
+        //计算当前行的行内卷积
+        for (int j = 1; j< w- 1;++j) {
+            buf[IDx(i + 1)][j] = src[i + 1][j - 1] * kx[0] + src[i + 1][j] * kx[1] + src[i + 1][j + 1] * kx[2];
+        }
+        //进行行间的卷积得到最终像素值
+        for (int j = 1; j< w - 1; ++j) {
+            dst[i][j] = buf[IDx(i - 1)][j] * ky[0] + buf[IDx(i)][j] * ky[1] + buf[IDx(i + 1)][j] * ky[2];
+        }
+    }
+}
+
+void applyOptimizedSeparableGaussianBlur(float src[][W], float dst[][W], int h, int w, float kx[3], float ky[3]) {
+
+    float buf[3][W+3];
+
+    float32x4_t kx_vec = vld1q_f32(kx); // 加载 kx
+    float32x4_t ky_vec = vld1q_f32(ky); // 加载 ky
+    for (int i = 0; i < 2; ++i) {
+        for (int j = 1; j < w - 1; j += 4) {
+            float32x4_t left = vld1q_f32(&src[i][j - 1]);
+            float32x4_t mid = vld1q_f32(&src[i][j]);
+            float32x4_t right = vld1q_f32(&src[i][j + 1]);
+
+            float32x4_t result = vmulq_lane_f32(left, vget_low_f32(kx_vec), 0); // kx[0] * left
+            result = vmlaq_lane_f32(result, mid, vget_low_f32(kx_vec), 1);// + kx[1] * mid
+            result = vmlaq_lane_f32(result, right, vget_high_f32(kx_vec), 0);// + kx[2] * right
+
+            vst1q_f32(&buf[i][j], result);
+        }
+    }
+
+    for (int i = 1; i < h - 1; ++i) {
+        //计算当前行的行内卷积
+        for (int j = 1; j< w- 1;++j) {
+            buf[IDx(i + 1)][j] = src[i + 1][j - 1] * kx[0] + src[i + 1][j] * kx[1] + src[i + 1][j + 1] * kx[2];
+            /*
+            float32x4_t left = vld1q_f32(&src[i+1][j - 1]);
+            float32x4_t mid = vld1q_f32(&src[i+1][j]);
+            float32x4_t right = vld1q_f32(&src[i + 1][j + 1]);
+
+            float32x4_t result = vmulq_lane_f32(left, vget_low_f32(kx_vec), 0); // kx[0] * left
+            result = vmlaq_lane_f32(result, mid, vget_low_f32(kx_vec), 1);// + kx[1] * mid
+            result = vmlaq_lane_f32(result, right, vget_high_f32(kx_vec), 0);// + kx[2] * right
+
+            vst1q_f32(&buf[IDx(i + 1)][j], result);
+            */
+        }
+        //进行行间的卷积得到最终像素值
+        for (int j = 1; j< w - 1; ++j) {
+
+
+            dst[i][j] = buf[IDx(i - 1)][j] * ky[0] + buf[IDx(i)][j] * ky[1] + buf[IDx(i + 1)][j] * ky[2];
+            /*
+            float32x4_t left = vld1q_f32(&buf[IDx(i - 1)][j]);
+            float32x4_t mid = vld1q_f32(&buf[IDx(i)][j]);
+            float32x4_t right = vld1q_f32(&buf[IDx(i + 1)][j]);
+
+            float32x4_t result = vmulq_lane_f32(left, vget_low_f32(ky_vec), 0); // kx[0] * left
+            result = vmlaq_lane_f32(result, mid, vget_low_f32(ky_vec), 1);// + kx[1] * mid
+            result = vmlaq_lane_f32(result, right, vget_high_f32(ky_vec), 0);// + kx[2] * right
+
+            vst1q_f32(&dst[i][j], result);
+            */
+        }
+    }
+
+
+}
+
+int main() {
+    float src_t[H][W]={0};
+    float dst1[H][W]={0};
+    float dst2[H][W]={0};
+    float dst3[H][W]={0};
+
+    float kernel[3][3] = {
+        {1.0f / 16, 2.0f / 16, 1.0f / 16},
+        {2.0f / 16, 4.0f / 16, 2.0f / 16},
+        {1.0f / 16, 2.0f / 16, 1.0f / 16}
+    };
+
+    float kx[3] = {0.25, 0.5, 0.25};
+    float ky[3] = {0.25, 0.5, 0.25};
+
+    for (auto & i : src_t) {
+        for (float & j : i) {
+            srand((unsigned)time(NULL));
+            j=0.01*rand();
+        }
+    }
+
+    clock_t start = clock();
+    applyGaussianBlur(src_t, dst1, H, W, kernel);
+    clock_t end = clock();
+    printf("耗时%lf秒\n",(double)(end-start)/CLOCKS_PER_SEC);
+
+    start = clock();
+    applySeparableGaussianBlur(src_t, dst2, H, W, kx,ky);
+    end = clock();
+    printf("耗时%lf秒\n",(double)(end-start)/CLOCKS_PER_SEC);
+
+    start = clock();
+    applyOptimizedSeparableGaussianBlur(src_t, dst3, H, W, kx,ky);
+    end = clock();
+    printf("耗时%lf秒\n",(double)(end-start)/CLOCKS_PER_SEC);
+
+
+    return 0;
+}
+