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.

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.

The pump heating effect of DFB fiber laser is normally ignored due to the short length of the laser cavity. However, by fabricating a phase-shifted FBG on high concentration Er-Yb codoped fiber to obtain a 16 mm long DFB fiber laser, the gradient surface temperature distributions along the active grating with different pump powers were observed. The average surface temperature rose by 16.82 K with a variation of less than 1.11 K, and the position with the highest temperature moved towards the center of the grating by 5.5 mm, when the pump power was increased from 0 mW to 191.6 mW. The transmission spectrum of the active phase-shifted FBG at different pump powers were measured, and an additional drift of the transmission peak in the stopband was testified. It was identified as an equivalent phase shift up to −0.1 π, which was induced by the gradient longitudinal temperature distribution. Considering that the initial phase shift of the grating was about 1.15 π, the increasing chirp of the active grating due to the pump heating could compensate the phase shift deviation from π surprisingly. The experimental results coincided with the simulation results by using the transmission matrix method under the assumption of piecewise-uniform structure for the chirped phase-shifted grating. The modified model of the active phase-shifted FBG reveals the difference between the cool cavity and the hot cavity at different pump powers, which may be used as a self-optimization mechanism for DFB fiber laser operation.

A diode-end-pumped self-mode-locked Tm,Ho:LuLiF₄ (LLF) laser is demonstrated for the first time, to the best of our knowledge. At the incident pump power of 3.4 W, the stable self-mode-locked operation of the Tm,Ho:LLF laser was realized without any additional devices in the resonator. Further increasing the incident pump power to 6.8 W, the maximum average output power of 1.07 W was achieved at 2068 nm with a pulse width of 746 ps and a repetition frequency of 468 MHz. The experimental results indicate that the Tm,Ho:LLF crystal is promising to generate the high-power self-mode-locked solid-state laser at 2 μm waveband. The self-mode-locked Tm,Ho:LLF laser has potential applications in optical communication, remote sensing, material process, and nonlinear frequency conversion.