Enhance Low Light Image using Dehazing Algorithm

导入一个RGB image captured in low light.

A = imread(“lowlight_11.jpg”); imshow(A);

Invert the image and notice how the low-light areas in the original image appear hazy.

AInv = imcomplement(A); imshow(AInv);

Reduce the haze using theimreducehazefunction.

BInv = imreducehaze(AInv); imshow(BInv);

Invert the results to obtain the enhanced image.

B = imcomplement(BInv);

Display the original image and the enhanced images, side-by-side.

montage({A,B});

Improve Results Further UsingimreducehazeOptional Parameters

To get a better result, callimreducehazeon the inverted image again, this time specifying some optional parameters.

BInv = imreducehaze(AInv,'Method','approx',“ContrastEnhancement”,'boost'); BImp = imcomplement(BInv); figure, montage({A, BImp});

Another Example of Improving Poorly Lit Image

导入一个RGB image captured in low light.

A = imread('lowlight_21.jpg');

Invert the image.

AInv = imcomplement(A);

Apply the dehazing algorithm.

BInv = imreducehaze(AInv,“ContrastEnhancement”,'none');

Invert the results.

B = imcomplement(BInv);

Display the original image and the enhanced images, side-by-side.

montage({A,B});

Reduce Color Distortion by Using Different Color Space

Convert the input image from the RGB colorspace to the L*a*b* colorspace.

Lab = rgb2lab(A);

Invert the L*a*b* image.

LInv = imcomplement(Lab(:,:,1) ./ 100);

Dehaze the inverted image using theimreducehazefunction.

LEnh = imcomplement(imreducehaze(LInv,“ContrastEnhancement”,'none'));

Increase the saturation.

LabEnh(:,:,1) = LEnh .* 100; LabEnh(:,:,2:3) = Lab(:,:,2:3) * 2;% Increase saturation

Convert the image back to an RGB image and display the original and the enhanced image, side-by-side.

AEnh = lab2rgb(LabEnh); montage({A,AEnh});

Improve Results Using Denoising

Low-light images can have high noise levels. Enhancing low-light images can increase this noise level. Denoising can be a useful post-processing step.

Use theimguidedfilterfunction to remove noise from the enhanced image.

B = imguidedfilter(BImp); montage({BImp,B});

Estimate Illumination Map

导入一个RGB image captured in low light.

A = imread('lowlight_21.jpg');

Invert the image.

AInv = imcomplement(A);

Apply the dehazing algorithm to the image.

[BInv,TInv] = imreducehaze(AInv,'Method','approxdcp',“ContrastEnhancement”,'none');

Invert the enhanced image.

T = imcomplement(TInv);

Display the original image next to the estimated illumination map in false color.

tiledlayout(1,2) nexttile imshow(A) title('Lowlight Image') nexttile imshow (T)标题('Illumination Map') colormap(hot)

Limitations

This method can lose some details or get over-enhanced because of poor adaptability of the dark channel in low-light conditions.

References

Dong, Xuan, et al. "Fast efficient algorithm for enhancement of low lighting video." Multimedia and Expo (ICME), 2011 IEEE International Conference on. IEEE, 2011.