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Because of the unique imaging mechanism of infrared (IR) sensors, IR images commonly suffer from blurred edge details, low contrast, and poor signal-to-noise ratio. A new method is proposed in this paper to enhance IR image details so that the enhanced images can effectively inhibit image noise and improve image contrast while enhancing image details. First, for the traditional guided image filter (GIF) applied to IR image enhancement is prone to halo artifacts, this paper proposes a detail enhancement guided filter (DGIF). It mainly adds the constructed edge perception and detail regulation factors to the cost function of the GIF. Then, according to the visual characteristics of human eyes, this paper applies the detail regulation factor to the detail layer enhancement, which solves the problem of amplifying image noise using fixed gain coefficient enhancement. Finally, the enhanced detail layer is directly fused with the base layer so that the enhanced image has rich detail information. We first compare the DGIF with four guided image filters and then compare the algorithm of this paper with three traditional IR image enhancement algorithms and two IR image enhancement algorithms based on the GIF on 20 IR images. The experimental results show that the DGIF has better edge-preserving and smoothing characteristics than the four guided image filters. The mean values of quantitative evaluation of information entropy, average gradient, edge intensity, figure definition, and root-mean-square contrast of the enhanced images, respectively, achieved about 0.23%, 3.4%, 4.3%, 2.1%, and 0.17% improvement over the optimal parameter. It shows that the algorithm in this paper can effectively suppress the image noise in the detail layer while enhancing the detail information, improving the image contrast, and having a better visual effect. [ABSTRACT FROM AUTHOR]

Due to the low spatial resolution of the existing optical micro-scanning thermal microscope imaging system, the acquired micro-scanning infrared images have inferior image quality and low contrast. Deep learning methods, represented by SRGAN, have shown promising results in super-resolution. However, this method still has artifacts, blurriness, low spatial resolution, and slow reconstruction speed. Therefore, we propose the SMC-SRGAN-Lightning super-resolution algorithm based on optical micro-scanning thermal microscope images in this study. Firstly, we enhance the model’s attention to features and improve the details and clarity of the reconstructed images. Removing the BN layer in residual blocks, replacing the ReLU with SMU, and introducing the CBAM to construct the SMC module. Secondly, we incorporate the attention mechanism SEnet into the Bottleneck structure of MobileNetV2. Reducing the channels in the first 1 × 1 convolution layer to 1/4 and creating the SE-MobileNetV2 module. It can enhance the model’s focus on essential features, computational efficiency, and accuracy. Finally, to validate the effectiveness of our method, we compare it with four other super-resolution algorithms on public datasets and images obtained from the optical micro-scanning thermal microscope imaging system. Experimental results indicate that our method improves image clarity, preserving details, and textures. Comprehensively considering super-resolved image quality and time costs, our method is superior to other methods. [ABSTRACT FROM AUTHOR]