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

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.