多尺度的retinex算法matlab

以下是一个多尺度的Retinex算法的Matlab代码示例：

function [output_image] = multiscale_retinex(input_image, sigma_list)

% This function performs multiscale Retinex algorithm on an input image
%
% INPUTS:
%   input_image: An RGB image of size MxNx3
%   sigma_list: A list of standard deviations (sigma) for Gaussian smoothing
%
% OUTPUTS:
%   output_image: The enhanced RGB image of size MxNx3

% Convert the input image to double precision
input_image = im2double(input_image);

% Initialize variables
output_image = zeros(size(input_image));

% Compute the multiscale Retinex for each channel
for c = 1:3
    channel = input_image(:,:,c);
    channel_msr = zeros(size(channel));
    for sigma = sigma_list
        % Apply Gaussian smoothing to the channel
        smoothed = imgaussfilt(channel, sigma);
        
        % Compute the log of the channel
        log_channel = log(channel + 1);
        
        % Compute the log of the smoothed channel
        log_smoothed = log(smoothed + 1);
        
        % Compute the difference between the logs
        diff = log_channel - log_smoothed;
        
        % Apply exponential function to the difference
        exp_diff = exp(diff);
        
        % Add the exponential difference to the multiscale Retinex
        channel_msr = channel_msr + exp_diff;
    end
    
    % Normalize the multiscale Retinex
    channel_msr = channel_msr / length(sigma_list);
    
    % Scale the channel by the multiscale Retinex
    output_image(:,:,c) = channel ./ channel_msr;
end

% Clip the output image to the range [0,1]
output_image = max(0, min(output_image, 1));

end

使用方法：

% Read the input image
input_image = imread('input_image.jpg');

% Define the sigma list
sigma_list = [15, 80, 250];

% Perform multiscale Retinex on the input image
output_image = multiscale_retinex(input_image, sigma_list);

% Display the input and output images
figure;
subplot(1,2,1); imshow(input_image); title('Input Image');
subplot(1,2,2); imshow(output_image); title('Output Image');