Laser speckle noise caused by coherence between lasers greatly influences the produced image. In order to suppress the effect of laser speckles on images, in this paper we set up a combination of a laser-structured light module and an infrared camera to acquire laser images, and propose an improved weighted non-local mean (IW-NLM) filtering method that adopts an SSI-based adaptive h-solving method to select the optimal h in the weight function. The analysis shows that the algorithm not only denoises the laser image but also smooths pixel jumps in the image, while preserving the image details. The experimental results show that compared with the original laser image, the equivalent number of looks (ENL) index of the IW-NLM filtered image improved by 0.80%. The speckle suppression index (SSI) of local images dropped from 4.69 to 2.55%. Compared with non-local mean filtering algorithms, the algorithm proposed in this paper is an improvement and provides more accurate data support for subsequent image processing analysis.

The utilization rate of ink liquid in the chamber is critical for the thermal bubble inkjet head. The difficult problem faced by the thermal bubble inkjet printing is how to maximize the use of ink in the chamber and increase the printing frequency. In this paper, by adding a flow restrictor and two narrow channels into the chamber, the H-shape flow-limiting structure is formed. At 1.8 μs, the speed of bubble expansion reaches the maximum, and after passing through the narrow channel, the maximum reverse flow rate of ink decreased by 25%. When the vapor bubble disappeared, the ink fills the nozzle slowly. At 20 μs, after passing through the narrow channel, the maximum flow rate of the ink increases by 39%. The inkjet printing frequency is 40 kHz, and the volume of the ink droplet is about 13.1 pL. The structure improves the frequency of thermal bubble inkjet printing and can maximize the use of liquid in the chamber, providing a reference for cell printing, 3D printing, bioprinting, and other fields.

Efficient printing frequency is critical for thermal bubble inkjet printing, while the difficulty lies in the structural design and material selection of the heating resistors. In this paper, a TaN film was used as the main material of the heating resistors, and two TaN films were placed in parallel to form the chopsticks-shaped structure. The heating time was divided into two sections, in which 0–0.1 μs was the preheating and 1.2–1.8 μs was the primary heating. At 1.8 μs, the maximum temperature of the Si₃N₄ film could reach about 1100 °C. At the same time, the SiO₂ film was added between the TaN film and Si₃N₄ film as a buffer layer, which effectively avoided the rupture of the Si₃N₄ film due to excessive thermal stress. Inside the inkjet print head, the maximum temperature of the chamber reached about 680 °C at 2.5 μs. Due to the high power of the heating resistors, the working time was greatly reduced and the frequency of the inkjet printing was effectively increased. At the interface between the back of the chip and the cartridge, the SiO₂ film was used to connect to ensure a timely ink supply. Under the condition of 12 V at 40 kHz, the inkjet chip could print efficiently with 10 nozzles at the same time. The inkjet chip proposed in this paper is not limited to only office printing, but also provides a new reference for 3D printing, cell printing, and vegetable and fruit printing.

Three-dimensional imaging plays a crucial role at the micro-scale in fields such as precision manufacturing and materials science. However, image noise significantly impacts the accuracy of point cloud reconstruction, making image denoising techniques a widely discussed topic. Statistical analysis of laser imaging noise has led to the conclusion that logarithmically transformed noise follows a Gumbel distribution. A corresponding neural network training set was developed to address the challenges of difficult data collection and the scarcity of phase-unwrapped image datasets. Building on this foundation, a phase-unwrapped image denoising method based on the Denoising Convolutional Neural Network (DnCNN) is proposed. This method aims to achieve three-dimensional filtering by performing two-dimensional image denoising. Experimental results show a significant reduction in the Cloud-to-Mesh Distance (C2M) statistics of the corresponding point clouds before and after planar filtering. Specifically, the statistic at 97.5% of the 2σ principle decreases from 0.8782 mm to 0.3384 mm, highlighting the effectiveness of the filtering algorithm in improving the planar fit. Moreover, the DnCNN method exhibits exceptional denoising performance when applied to real-world target data, such as plaster statues with complex depth variations and PCBs made from different materials, thereby enhancing accuracy and reliability in point cloud reconstruction. This study provides valuable insights into phase-unwrapped image noise suppression in laser imaging, particularly in micro-scale applications where precision is critical.