Low-light images are not conducive to human observation and computer vision algorithms due to their low visibility. To solve this problem, many image enhancement techniques have been proposed. However, existing techniques inevitably introduce color and lightness distortion when increasing visibility. To lower the distortion, we propose a novel enhancement method using the response characteristics of cameras. First, we investigate the relationship between two images with different exposures to obtain an accurate camera response model. Then we borrow the illumination estimation techniques to estimate the exposure ratio map. Finally, we use our camera response model to adjust each pixel to its desired exposure according to the estimated exposure ratio map. Experiments show that our method can obtain enhancement results with less color and lightness distortion compared to several state-of-the-art methods. 

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@InProceedings{Ying_2017_ICCV,
author = {Ying, Zhenqiang and Li, Ge and Ren, Yurui and Wang, Ronggang and Wang, Wenmin},
title = {A New Low-Light Image Enhancement Algorithm Using Camera Response Model},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {Oct},
year = {2017}
}

Paper: ICCV Open Access versions | ResearchGate

Matlab Implementation:

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function J = Ying_2017_ICCV(I, model) 
if ~isfloat(I), I = im2double(I); end
%% Param Settings
alpha = 1;
eps = 0.001;
range = 5;
ratioMax = 7;
%% Camear Model
% we use beta-gamma model as default
if ~exist('model', 'var'), model = 'beta-gamma'; end
switch lower(model)
    case 'preferred' 
        param = {4.3536    1.2854    0.1447};
        [a, b, c] = deal(param{:});
        g = @(I,k)(I.*(k.^(a.*c)) )./ (( (I).^(1./c) .*(k.^a - 1)   + 1).^c);
        
    case 'beta-gamma'
    	param = {-0.3293    1.1258};
    	[a, b] = deal(param{:});
        f = @(x)exp((1-x.^a).*b);
        g = @(I,k)I.^(k.^a).*f(k);
        
    case 'affine'
         param = {-0.1797    1.2076    0.3988};
         [alpha, beta, gamma] = deal(param{:});
         g = @(I,k)I.*(k.^gamma) + alpha.*(1-k.^gamma);
         
    case 'beta'
         c = 0.4800;
         g = @(I,k)I.*( k.^c );
         
    otherwise 
        error('unkown model: %s', model);
end
%% Exposure Ratio Map
% Initial Exposure Ratio Map
t0 = max(I, [], 3);
[M,N] = size(t0);
% Exposure Ratio Map Refinement
t0 = imresize(t0,0.5);
dt0_v = [diff(t0,1,1);t0(1,:)-t0(end,:)];
dt0_h = [diff(t0,1,2)';t0(:,1)'-t0(:,end)']';
gauker_h = filter2(ones(1,range),dt0_h);
gauker_v = filter2(ones(range,1),dt0_v);
W_h = 1./(abs(gauker_h).*abs(dt0_h)+eps);
W_v = 1./(abs(gauker_v).*abs(dt0_v)+eps);
T = solveLinearEquation(t0,W_h,W_v,alpha./2);
T = imresize(T,[M,N]);
T = repmat(T, [1 1 3]);
kRatio = min(1./T,ratioMax);
%% Enhancement
J = g(I, kRatio);
J = max(0, min(1, J));
end
function OUT = solveLinearEquation( IN, wx, wy, lambda )
[ r, c, ch ] = size( IN );
k = r * c;
dx =  -lambda * wx( : );
dy =  -lambda * wy( : );
tempx = [wx(:,end),wx(:,1:end-1)];
tempy = [wy(end,:);wy(1:end-1,:)];
dxa = -lambda *tempx(:);
dya = -lambda *tempy(:);
tempx = [wx(:,end),zeros(r,c-1)];
tempy = [wy(end,:);zeros(r-1,c)];
dxd1 = -lambda * tempx(:);
dyd1 = -lambda * tempy(:);
wx(:,end) = 0;
wy(end,:) = 0;
dxd2 = -lambda * wx(:);
dyd2 = -lambda * wy(:);
Ax = spdiags( [dxd1,dxd2], [-k+r,-r], k, k );
Ay = spdiags( [dyd1,dyd2], [-r+1,-1], k, k );
D = 1 - ( dx + dy + dxa + dya);
A = (Ax+Ay) + (Ax+Ay)' + spdiags( D, 0, k, k );
if exist( 'ichol', 'builtin' )
    L = ichol( A, struct( 'michol', 'on' ) );
    OUT = IN;
    for ii = 1:ch
        tin = IN( :, :, ii );
        [ tout, flag ] = pcg( A, tin( : ), 0.1, 50, L, L' );
        OUT( :, :, ii ) = reshape( tout, r, c );
    end
else
    OUT = IN;
    for ii = 1:ch
        tin = IN( :, :, ii );
        tout = A\tin( : );
        OUT( :, :, ii ) = reshape( tout, r, c );
    end
end
end

Try it:

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I = imread('yellowlily.jpg');
J = Ying_2017_ICCV(I); 
subplot 121; imshow(I); title('Original Image');
subplot 122; imshow(J); title('Enhanced Result');