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.
Highlights
Image denoising is a very important part of image processing, and it is the basis of all subsequent processing, such as image recognition [1], object detection and tracking [2]
A non-reference image denoising method based on enhanced dual-tree complex wavelet optimized by fruit fly algorithm and bilateral filter (IFOA-dual-tree complex wavelet transform (DTCWT)-BF) is proposed and the method is compared with DTCWT and other denoising algorithms
An adaptive dual-tree complex wavelet threshold denoising method for non-reference image denoising based on improved fruit fly algorithm and bilateral filter is proposed in this paper, and a series of simulations and applications prove the effectiveness of the method
Summary
Image denoising is a very important part of image processing, and it is the basis of all subsequent processing, such as image recognition [1], object detection and tracking [2]. Spatial domain filters have high processing speed since they operate the pixels on the original images, such as median filter [3], wiener filter [4] and bilateral filter (BF) [5] These algorithms can remove noise quickly, but the denoising abilities are mediocre. These methods cannot reach the best denoising performance since the parameters are fixed. A non-reference image denoising method based on enhanced dual-tree complex wavelet optimized by fruit fly algorithm and bilateral filter (IFOA-DTCWT-BF) is proposed and the method is compared with DTCWT and other denoising algorithms.
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