假設一幅大小爲500 * 500的圖像擴大1.5倍到750 * 750,建立一個750 * 750 的網格,使其與原圖像間隔相同,而後縮小至原圖大小,在原圖中尋找最接近的像素(或周圍的像素)進行賦值,最後再將結果放大spa
尋找最近的像素賦值3d
v(x,y) = ax + by + cxy + dcode
已知Q11, Q12, Q21, Q22,要插值的點爲P點,首先在x軸上,對R1,R2兩個點進行插值blog
而後根據R1和R2對P點進行插值io
化簡得class
對於邊界值的處理,若x1 < 0 ,則直接令f(Q11), f(Q12) = 0im
int is_in_array(short x, short y, short height, short width) { if (x >= 0 && x < width && y >= 0 && y < height) return 1; else
return 0; } void bilinera_interpolation(short** in_array, short height, short width, short** out_array, short out_height, short out_width) { double h_times = (double)out_height / (double)height, w_times = (double)out_width / (double)width; short x1, y1, x2, y2, f11, f12, f21, f22; double x, y; for (int i = 0; i < out_height; i++){ for (int j = 0; j < out_width; j++){ x = j / w_times; y = i / h_times; x1 = (short)(x - 1); x2 = (short)(x + 1); y1 = (short)(y + 1); y2 = (short)(y - 1); f11 = is_in_array(x1, y1, height, width) ? in_array[y1][x1] : 0; f12 = is_in_array(x1, y2, height, width) ? in_array[y2][x1] : 0; f21 = is_in_array(x2, y1, height, width) ? in_array[y1][x2] : 0; f22 = is_in_array(x2, y2, height, width) ? in_array[y2][x2] : 0; out_array[i][j] = (short)(((f11 * (x2 - x) * (y2 - y)) + (f21 * (x - x1) * (y2 - y)) + (f12 * (x2 - x) * (y - y1)) + (f22 * (x - x1) * (y - y1))) / ((x2 - x1) * (y2 - y1))); } } }