The current deep learning-based image fusion methods can not sufficiently learn the features of images in a wide frequency range. Therefore, we proposed IFormerFusion, which is based on the Inception Transformer and cross-domain frequency fusion. To learn features from high- and low-frequency information, we designed the IFormer mixer, which splits the input features through the channel dimension and feeds them into parallel paths for high- and low-frequency mixers to achieve linear computational complexity. The high-frequency mixer adopts a convolution and a max-pooling path, while the low-frequency mixer adopts a criss-cross attention path. Considering that the high-frequency information relates to the texture detail, we designed a cross-domain frequency fusion strategy, which trades high-frequency information between the source images. This structure can sufficiently integrate complementary features and strengthen the capability of texture retaining. Experiments on the TNO, OSU, and Road Scene datasets demonstrate that IFormerFusion outperforms other methods in object and subject evaluations.

Pulsed laser range-gated imaging (PLRGI) is one of the most effective methods to achieve underwater high-resolution imaging. When searching target, there are two methods can be used: fixed gate and sliding gate. In practice, fixed gate has a small depth of field of view and sliding gate cannot meet the need of real time. In order to overcome these problems, multi-slice integration (MSI) method is proposed in this paper. First, the laser energy received by the PLRGI system is derived from radiative transfer theory. In addition, range intensity profiles (RIP) of MSI method is established. Experiments are carried out in lab to validate the RIP model and results show that theoretical data and experimental data are in good coincidence. Then the gate width and the number of pulse assigned to each slice are discussed. Finally, to prove the effectiveness of the MSI method, experiments are carried out in a boat tank. Results show that the MSI method is better than the fixed gate method for surveillance, and can acquire a clear image of the target at 18 m (4.5 times of the attenuation length) in the water with attenuation coefficient of 0.25 m⁻¹.

Active illumination underwater brings the backscatter problem. The traditional pulsed laser range-gated imaging (PLRGI) system uses a small gate width to suppress most of the backscattered light. As a result, the depth of field (DOF) of the PLRGI system is small. In addition, the DOF has to be extended when the system is used for searching the target. There are several methods to extend the DOF. However, every method has its shortcoming. To overcome these problems, a multi-slice integration (MSI) method is proposed. In turbid water, the image captured by the MSI method also has the backscatter problem. The rolling ball and intensity transformation are proposed as an image enhancement method to remove the backscattered light and improve the brightness of the image. Experiments carried out in a towing boat tank and Mulan Lake show that the proposed method is better than lower-upper-threshold correlation, dark channel priors, and unsharp filtering for backscattered light removal.

The multi-slice integration (MSI) method is one of the approachs to extend the depth of view (DOV) of the pulsed laser range-gated imaging (PLRGI) system. When the DOV is large enough and exceeds the depth of focus of the system, it may make some targets in the image clear and others blurred. In addition, forward scatter is also considered to have a blurring effect on the image. There is very little literature to solve the combined effect of forward scatter and defocus. An imaging model is built based on the model from Jaffe–McGlamery and Fourier optics. According to the imaging model, backscattered light is independent from reflected light from the target, and forward scatter has a relationship with the reflected light. Thus, backscattered light should be removed before deblurring. First, rolling ball and intensity transformation are used to remove the backscattered light and enhance the image. Then, a deep learning model based on Transformer is used to deblur the image. To enable the deep learning model to accommodate different degrees of blurred image, 16 different blur kernels are generated according to the imaging model. Sharp images from a DPDD dataset were chosen to train the model. Images of varying degrees of blur were collected from a water tank and a boat tank by the PLRGI system as test sets. Image deblurring results show that the proposed method can remove different levels of blur and can deal with images which have sharp targets and blurred targets together.

In this paper, we propose an infrared and visible image fusion network based on RepVGG architecture. This network adopts an encoder-decoder structure. The encoding network, which contains five RepVGG blocks, is utilized to extract deep features of infrared and visible images. Each layer of RepVGG blocks is constructed with 3x3, 1x1 and identity branches while training and converted to single-branch architecture constructed with 3x3 convolutional layers while inferring. These extracted features are added and the fusion image is reconstructed by the decoding network. The proposed method was compared with seven fusion methods and the result shows that the proposed fusion method can retain more contour and texture information with less noise. The proposed method is superior to the comparison methods. The code of the proposed fusion network is available at https://github.com/xiongzhangzzz/repvggfuse.