A Non-Reference Image Denoising Method for Infrared Thermal Image Based on Enhanced Dual-Tree Complex Wavelet Optimized by Fruit Fly Algorithm and Bilateral Filter

Abstract

Abstract:To eliminate the noise of infrared thermal image without reference and noise model, an improved dual-tree complex wavelet transform (DTCWT), optimized by an improved fruit-fly optimization algorithm (IFOA) and bilateral filter (BF), is proposed in this paper. Firstly, the noisy image is transformed by DTCWT, and the noise variance threshold is optimized by the IFOA, which is enhanced through a fly step range with inertia weight. Then, the denoised image will be re-processed using bilateral filter to improve the denoising performance and enhance the edge information. In the experiment, the proposed method is applied to eliminate both addictive noise and multiplicative noise, and the denoising results are compared with other representative methods, such as DTCWT, block-matching and 3D filtering (BM3D), median filter, wiener filter, wavelet decomposition filter (WDF) and bilateral filter. Moreover, the proposed method is applied as pre-processing utilization for infrared thermal images in a coal mining working face.

Application of Artificial Neural Network for Image Noise Level Estimation in the SVD domain

“…For each image block, the noise level is selected randomly from a uniform distribution in the interval (0, 35). Subsequently, a feature vector is derived from each image block using Equation (5). The input and the target training datasets are defined as a set of 50000 feature vectors and a set of associated noise levels, respectively.…”

“…In this paper, the problem of noise level estimation is considered, whereby the principal objective is to ascertain the level of noise that is present in a noisy digital image. In general, the blind estimation of noise levels is an important area of digital image processing with numerous applications including image denoising [1][2][3][4][5][6], edge detection [7], super-resolution image reconstruction [8], image segmentation [9,10], and feature extraction [11,12]. Images are commonly degraded by noise during image acquisition, transmission, storage, and image processing [1,13].…”

A Robust Brain MRI Segmentation and Bias Field Correction Method Integrating Local Contextual Information into a Clustering Model

“…where I w (x) is the denoising image after using the anisotropic weighting scheme. The expressions of b(x) and J(x) described in Equations (15) and (16) can be introduced to minimize this energy function effectively. Then, the energy function can be re-expressed as an equation with respect to three independent variables w = (w 1 ,…”

“…Magnetic resonance (MR) image segmentation is a key step after magnetic resonance imaging (MRI), and its results directly affect diagnosis and treatment [1][2][3]. Numerous models have been proposed to achieve this, including clustering models [4][5][6], level set models [7][8][9][10], active contour models [11,12], and so on [13][14][15]. Accurate segmentation results have important guiding significance for clinical diagnosis.…”

Special Issue on ‘Novel Ideas for Infrared Thermography and Its Application to Integrated Approaches’

“…In the experiment, the proposed method is applied to eliminate both addictive noise and multiplicative noise, while the denoising results were compared to other representative methods. Finally, the proposed method was applied as a pre-processing utilization for infrared thermal images in a coal mining working face [28].…”