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imreducehaze

Reduce atmospheric haze

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Syntax

[J,T,L] = imreducehaze(I)
[___] = imreducehaze(I,amount)
[___] = imreducehaze(___,Name,Value)

Description

[J,T,L] = imreducehaze(I) reduces atmospheric haze in color or grayscale image I. The function returns the dehazed image J, an estimate T of the haze thickness at each pixel, and the estimated atmospheric light L.

example

[___] = imreducehaze(I,amount) additionally specifies the amount of haze to remove.

[___] = imreducehaze(___,Name,Value) changes the behavior of the dehazing algorithm using name-value arguments.

example

Examples

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Open Live Script

Read a hazy image into the workspace.

A = imread('foggysf1.jpg');

Reduce the haze and display the result next to the original image in a montage.

B = imreducehaze(A);
montage({A,B})
title("Hazy Image (Left) vs. Reduced Haze Image (Right)")

Figure contains an axes object. The hidden axes object with title Hazy Image (Left) vs. Reduced Haze Image (Right) contains an object of type image.

Open Live Script

Read a hazy image into the workspace.

A = imread('foggysf2.jpg');

Reduce 90% of the haze using the approxdcp method.

B = imreducehaze(A,0.9,'method','approxdcp');

Display in a montage the original hazy image and the image with reduced haze.

montage({A,B})

Figure contains an axes object. The hidden axes object contains an object of type image.

Open Live Script

Read and display a hazy image.

A = imread('foggyroad.jpg');
imshow(A)
title('Hazy Image')

Figure contains an axes object. The hidden axes object with title Hazy Image contains an object of type image.

Reduce haze in the image using default parameter values. Return an estimate of the haze thickness.

[~,T] = imreducehaze(A);

Display the haze thickness measurement.

imshow(T)
title('Haze Thickness')

Figure contains an axes object. The hidden axes object with title Haze Thickness contains an object of type image.

The haze thickness T provides a rough approximation of the depth D of the scene, defined up to an unknown multiplication factor. Add eps to avoid log(0).

D = -log(1-T+eps);

Display the estimated depth in false color.

imshow(D,[])
title('Depth Estimate')
colormap hot

Figure contains an axes object. The hidden axes object with title Depth Estimate contains an object of type image.

Input Arguments

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Hazy image, specified as an RGB or grayscale image.

Data Types: single | double | uint8 | uint16

Amount of haze to remove, specified as a number in the range [0, 1]. When the value is 1, imreducehaze reduces the maximum amount of haze. When the value is 0, imreducehaze does not reduce haze and the input image is unchanged. Larger values can cause more severe color distortion.

Data Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64

Name-Value Arguments

Specify optional pairs of arguments as Name1=Value1,...,NameN=ValueN, where Name is the argument name and Value is the corresponding value. Name-value arguments must appear after other arguments, but the order of the pairs does not matter.

Example: B = imreducehaze(A,0.9,method="approxdcp"); reduces haze using the approximate dark channel prior method.

Before R2021a, use commas to separate each name and value, and enclose Name in quotes.

Example: B = imreducehaze(A,0.9,"method","approxdcp");

Technique used to reduce haze, specified as one of these values:

  • "simpledcp" — Simple dark channel prior method [2]. This technique uses a per-pixel dark channel to estimate haze and quadtree decomposition to estimate the atmospheric light.

  • "approxdcp" — Approximate dark channel prior method [1]. This technique uses both per-pixel and spatial blocks when computing the dark channel and does not use quadtree decomposition.

For more information, see Algorithms.

Data Types: char | string

Maximum value to be treated as haze, specified as a 1-by-3 numeric vector for RGB images or a numeric scalar for grayscale images. Values must be in the range [0, 1]. Atmospheric light values greater than 0.5 tend to give better results.

If you do not specify AtmosphericLight, then the imreduzehaze function estimates a value depending on the value of Method.

Data Types: double

Contrast enhancement technique, specified as "global", "boost", or "none".

Data Types: char | string

Amount of per-pixel gain to apply as postprocessing, specified as a positive number in the range (0, 1]. This argument is only supported if ContrastEnhancement is specified as "boost".

Data Types: double

Output Arguments

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Dehazed image, returned as numeric array of the same size as the input hazy image I.

Haze thickness estimated at each pixel, returned as a numeric array.

Estimated atmospheric light, returned as a numeric array. L represents the value of the brightest nonspecular haze.

Algorithms

The model to describe a hazy image I is

I(x) = J(x)T(x) + L(1-T(x))
I is the observed intensity, J is the scene radiance, L is atmospheric light, and T is a transmission map describing the portion of light that reaches the camera.

Dehazing algorithms recover the scene radiance (dehazed image) J from an estimation of the transmission map and atmospheric light according to:

J(x) = (I(x)-A)/(max(t(x),t0)) + A

imreducehaze uses two different dehazing algorithms, simpledcp and approxdcp. These methods both rely on a dark channel prior, which is based on the observation that nonhazy images of outdoor scenes usually contain some pixels that have low signal in one or more color channels. The methods differ in how they estimate the dark channel prior and atmospheric light.

The dehazing algorithms in imreducehaze follow five steps:

  1. Estimate the atmospheric light L using a dark channel prior.

  2. Estimate the transmission map T.

  3. Refine the estimated transmission map.

  4. Restore the image.

  5. Perform optional contrast enhancement.

References

[1] He, Kaiming. "Single Image Haze Removal Using Dark Channel Prior." Thesis, The Chinese University of Hong Kong. 2011.

[2] Dubok, et al. "Single Image Dehazing with Image Entropy and Information Fidelity." ICIP. 2014, pp. 4037–4041.

Extended Capabilities

Version History

Introduced in R2017b

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See Also

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