【图像去噪】基于快速跨尺度小波降噪实现泊松损坏图像去噪处理附matlab代码

最新推荐文章于 2025-05-13 15:59:36 发布

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最新推荐文章于 2025-05-13 15:59:36 发布

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该文介绍了一种针对低光照生物显微成像中Poisson噪声的图像去噪新算法。由于低光照条件下的成像通常受到强信号依赖的 shot noise 限制，非加性非高斯模型能提供更好的恢复效果。文章提出了一种名为PURELET的算法，旨在在保证恢复质量的同时，兼顾计算复杂性和自动化程度，尤其适合处理大型数据集，并且需要最少的人工输入，以提高结果的可重复性。

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1 简介

‘‘Additive white Gaussian noise’’ is a ubiquitous model in the context of statistical image restoration. In many applications, however, the current trend towards quanti tative imaging calls for less generic models that better account for the physical acquisition process. The need for such models is particularly stringent in biomicroscopy, where live samples are often observed at very low light levels, due to acquisition-time and phototoxicity constraints. In this regime, the performance of the imaging device is typically shot-noise limited, i.e., the measure ment noise is strongly signal dependent. Thus, opting for a non-additive, non-Gaussian model can yield signifificant restoration-quality improvements in such applications. In this paper we present a novel algorithmic solution for denoising low-count images. Motivated by practical applications in biomedical imaging, we aim at a tractable

compromise between restoration quality, computational complexity and automation. Specififically, our goals are the following. First, we want a method that is designed for Poissonian noise, which is the most common model for low-intensity imaging. Second, the method should satisfy strict constraints in terms of computational cost and

memory requirements, so as to be able to process large data sets. Finally, we want the algorithm to require as less

user input as possible in order to facilitate its application

and to enhance the reproducibility of its results.

2 部分代码

% ------------------------------------------------------------------------% Function: PURELET for Poisson image denoising with cycle spinning% ------------------------------------------------------------------------% Usage 1: XHAT = CSPIN_PURELET( X, LET_ID, J, nSpin )%% Input parameters:% X = Poisson noisy input image % LET_ID = LET ID; should be 0, 1, or 2. See [1].% J = No. of Haar wavelet scales.% nSpin = No. of shifts. The first one is [0 0] by default. Remaining%         nSpin-1 shifts are randomly computed using rand function.%  % Output parameters:% XHAT = Estimated image% % Description: Denoises a Poisson-count noisy image using PURELET with% cycle spinning% ------------------------------------------------------------------------% References:% [1] F. Luisier, C. Vonesch, T. Blu, M. Unser, "Fast Interscale Wavelet%     Denoising of Poisson-corrupted Images", Signal Processing, vol. 90,%     no. 2, pp. 415-427, February 2010.% ------------------------------------------------------------------------% Author: Sandeep Palakkal (sandeep.dion@gmail.com)% Affiliation: Indian Institute of Technology Madras% Created on: Feb 11, 2011% Modified on: Mar 19, 2011% ------------------------------------------------------------------------function y = cspin_purelet(  x, let_id, J, nSpin )[m n] = size( x );shifts = round( rand(nSpin-1,2).*repmat( [m n], nSpin-1, 1 ) );shifts = [0 0; shifts];y = zeros( m, n );for ii = 1:nSpin  y1 = purelet( circshift( x, shifts(ii,:) ), let_id, J );  y = y + circshift( y1, -shifts(ii,:) );endy = y/nSpin;