【图像评价】基于 WCID 算法实现水下彩色图像质量评估附Matlab代码和报告

原创于 2025-12-05 21:21:54 发布 · 345 阅读

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#算法 #matlab #开发语言

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🔥 内容介绍

本文提出的 WCID 算法，沿与上述水下图像形成路径相反的方向进行处理，具体流程如图 3 所示。首先，需考虑人工光源的可能存在及其影响；其次，去除从目标物体到相机的传播过程中产生的光散射与颜色变化；最后，针对光线穿越水深到达图像顶部这一过程，补偿波长衰减的差异，并通过为图像内每个像素点求解更精确的深度值，实现能量损失的微调。

在海洋工程领域，获取水下环境的清晰图像是一项重要课题 [1][2]。水下图像质量对各类科学任务起着关键作用，例如海洋生物监测、种群数量调查以及地质或生物环境评估等。

水下成像面临诸多挑战，主要源于两方面：一是 “水 haze（水雾 / 水浊度）”—— 光线从水面反射后，会被水中微粒偏转和散射；二是 “颜色变化”—— 不同波长的光线衰减程度不同，导致图像颜色发生改变 [3]-[5]。

光散射与颜色变化会使水下获取的图像出现对比度降低和颜色偏差的问题。以图 1 为例：鲹鱼群、潜水员以及背景中的暗礁所呈现的朦胧感，正是光散射造成的；而底部棕色珊瑚礁和右上角黄色鱼类呈现出蓝色调，则是颜色变化导致的结果。

⛳️ 运行结果

📣 部分代码

function [ imgout ] = pyr_expand( img )

%PYR_EXPAND Image pyramid expansion

% B = PYR_EXPAND( A ) If A is M-by-N, then the size of B

% is (2*M-1)-by-(2*N-1). Support gray or rgb image.

% B will be transformed to double class.

kw = 5; % default kernel width

cw = .375; % kernel centre weight, same as MATLAB func impyramid. 0.6 in the Paper

ker1d = [.25-cw/2 .25 cw .25 .25-cw/2];

kernel = kron(ker1d,ker1d')*4;

% expand [a] to [A00 A01;A10 A11] with 4 kernels

ker00 = kernel(1:2:kw,1:2:kw); % 3*3

ker01 = kernel(1:2:kw,2:2:kw); % 3*2

ker10 = kernel(2:2:kw,1:2:kw); % 2*3

ker11 = kernel(2:2:kw,2:2:kw); % 2*2

img = im2double(img);

sz = size(img(:,:,1));

osz = sz*2-1;

imgout = zeros(osz(1),osz(2),size(img,3));

for p = 1:size(img,3)

img1 = img(:,:,p);

img1ph = padarray(img1,[0 1],'replicate','both'); % horizontally padded

img1pv = padarray(img1,[1 0],'replicate','both'); % horizontally padded

img00 = imfilter(img1,ker00,'replicate','same');

img01 = conv2(img1pv,ker01,'valid'); % imfilter doesn't support 'valid'

img10 = conv2(img1ph,ker10,'valid');

img11 = conv2(img1,ker11,'valid');

imgout(1:2:osz(1),1:2:osz(2),p) = img00;

imgout(2:2:osz(1),1:2:osz(2),p) = img10;

imgout(1:2:osz(1),2:2:osz(2),p) = img01;

imgout(2:2:osz(1),2:2:osz(2),p) = img11;

end

🔗 参考文献

[1] K. Lebart, C. Smith, E. Trucco, and D. M. Lane, “Automatic indexing of underwater survey video: algorithm and benchmarking method,” IEEE J. Ocean. Eng., vol. 28, no. 4, pp. 673–686, Oct. 2003.

[2] J. Yuh and M. West, “Underwater robotics,” Adv. Robot., vol. 15, no.5, pp. 609–639, 2001.

[3] J. R. Zaneveld and W. Pegau, “Robust underwater visibility param- eter,” Opt. Exp., vol.11, no. 23, pp. 2997–3009, 2003.

[4] E. Trucco and A. T. Olmos-Antillon, “Self-tuning underwater image restoration,” IEEE J.Ocean. Eng., vol. 31, no. 2, pp. 511–519, Apr. 2006.

[5] J. S. Jaffe, “Computer modeling and the design of optimal underwater imaging systems,”IEEE J. Ocean. Eng., vol. 15, no. 2, pp. 101–111, Apr. 1990.

[6] M. C. W. van Rossum and T. M. Nieuwenhuizen, “Multiple scattering of classical waves:

Microscopy, mesoscopy and diffusion,” Rev. Modern Phys., vol. 71, no. 1, pp. 313–371, Jan.1999.

[7] Y. Y. Schechner and N. Karpel, “Recovery of underwater visibility and structure by

polarization analysis,” IEEE J. Ocean. Eng., vol. 30, no. 3, pp. 570–587, Jul. 2005.