引导滤波verilog代码

引导滤波（Guided Filter）是一种用于图像处理的滤波算法，可以用于图像去噪、图像增强等应用中。以下是一个简单的引导滤波Verilog代码示例。

module guided_filter (
  input clk,
  input rst,
  input [7:0] I_in,
  input [7:0] p_in,
  input [7:0] eps_in,
  input [7:0] r_in,
  output reg [7:0] q_out
);

reg [7:0] mean_I = 0;
reg [7:0] mean_p = 0;
reg [7:0] mean_Ip = 0;
reg [7:0] mean_II = 0;
reg [7:0] mean_pp = 0;
reg [7:0] mean_Iq = 0;
reg [7:0] mean_pq = 0;
reg [7:0] count = 0;

parameter N = 3;

always @(posedge clk) begin
  if (rst == 1) begin
    mean_I <= 0;
    mean_p <= 0;
    mean_Ip <= 0;
    mean_II <= 0;
    mean_pp <= 0;
    mean_Iq <= 0;
    mean_pq <= 0;
    count <= 0;
  end else begin
    if (count < N * N) begin
      mean_I <= mean_I + I_in;
      mean_p <= mean_p + p_in;
      mean_Ip <= mean_Ip + I_in * p_in;
      mean_II <= mean_II + I_in * I_in;
      mean_pp <= mean_pp + p_in * p_in;
      count <= count + 1;
    end else begin
      count <= count + 1;
      if (count == (N + 1) * (N + 1)) begin
        count <= 0;
      end
      q_out <= (mean_Ip - mean_I * mean_p / (N * N)) * (mean_II - mean_I * mean_I / (N * N) + eps_in) / (mean_II - mean_I * mean_I / (N * N) + eps_in * N * N) + (mean_p - mean_I * mean_pp / (N * N)) * (mean_Iq - mean_I * mean_pq / (N * N)) / (mean_II - mean_I * mean_I / (N * N) + eps_in * N * N);
      mean_I <= mean_I + I_in - I_in(N - 1)(N - 1);
      mean_p <= mean_p + p_in - p_in(N - 1)(N - 1);
      mean_Ip <= mean_Ip + I_in * p_in - I_in(N - 1)(N - 1)p_in(N - 1)(N - 1);
      mean_II <= mean_II + I_in * I_in - I_in(N - 1)(N - 1)I_in(N - 1)(N - 1);
      mean_pp <= mean_pp + p_in * p_in - p_in(N - 1)(N - 1)p_in(N - 1)(N - 1);
      mean_Iq <= mean_Iq + I_in * q_out - I_in(N - 1)(N - 1)q_out(N - 1)(N - 1);
      mean_pq <= mean_pq + p_in * q_out - p_in(N - 1)(N - 1)q_out(N - 1)(N - 1);
    end
  end
end

endmodule

这个代码模块包括了一个时钟信号clk、一个重置信号rst，以及输入图像I、引导图像p、eps和半径r，输出图像q。该模块使用了九点平均值的滑动窗口，计算出了引导滤波器所需要的中间变量mean_I、mean_p、mean_Ip、mean_II、mean_pp、mean_Iq和mean_pq。然后，使用这些中间变量计算出了输出值q_out。

请注意，这个代码示例仅用于说明引导滤波的基本实现思路，实际情况下可能需要进行更多的优化和调整。例如，在实际应用中，可能需要使用更大的滑动窗口，或者使用流水线等技术来提高计算效率。