Infrared imaging technology is widely used in many fields, such as industry military, and medical. The infrared imaging system uses a detector to measure the temperature difference between the target and the background and obtain the infrared image. Compared with visible cameras, infrared cameras can capture more critical information in particular environments, such as darkness, mist, and snow . As a special type of infrared camera, the mini-infrared thermal imaging system (MITIS), due to the advantages of low power consumption, small size, easy-to-carry, etc., has been widely used in medical, security, military, and other fields. However, due to the long imaging wavelength, environmental temperature influence, and imaging system limitation, infrared images of MITIS usually encounter the problems of low quality and high noise, which limits the applications and development of MITIS.
In recent years, many methods have been proposed for infrared image denoising. These methods can be divided into two categories. The first type is based on the filter, wavelet, and transform methods. For example, Chen et al. proposed a variance-stabilizing transform (VST) to convert the mixed noise into Gaussian noise and designed a dual-domain filter (DDF) to denoise transformed noises. Shao et al. presented the least square and gradient-domain guided filtering for removing vertical stripe noises in infrared images. Shen et al. designed an improved Anscombe transformation to transform the noise distribution from Poisson to Gaussian. Then, they used the improved total variation regularization method to suppress the noise with the optimal wavelet function. Chen et al. reported a dual-tree complex wavelet transform (DT-CWT) and Maximum likelihood estimation method to remove noises in the infrared image. The above methods convert the actual noise into standard noise distribution by transforming and then designing filters to remove the noise. Moreover, the standard noise distribution can not denote the actual noise, which will result in incomplete denoising.
The Second type is based on the convolutional neural network (CNN). As gray images are similar to infrared images, many gray image denoising methods have been used in infrared image denoising tasks. For instance, Zhang et al. developed a denoising CNN (DnCNN) incorporating multi-layer convolutions to predict the noise image. Zhang et al. designed FFDNet based on down-sampled sub-images that can enlarge the receptive field to improve denoising performance. Wang et al. designed the k-Sigma Transform to remove a wide range of noise levels. Guo et al. proposed a convolutional blind denoising network (CBDNet) that uses asymmetric learning to improve the noise prediction ability. Anwar et al. reported a single-stage blind real image denoising network (RIDNet), where an enhancement attention module is employed to provide broad receptive fields. Although the above methods can effectively remove infrared image noise, these methods have the following drawbacks: they lose the detail feature while denoising; they cannot effectively extract detailed features hidden in the background; the gray denoising methods cannot be directly applied to the infrared image to achieve excellent denoising performance.
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