To accurately capture small defect information in insulators,improve the reconstruction quality of terahertz images,and achieve precise insulator defect detection,a method for insulator defect detection based on terahertz pulse time-domain feature reconstruction algorithm was studied.This method combined improved Hilbert Huang transform and ensemble empirical mode decomposition to perform deep analysis of terahertz time-domain signals of insulators and accurately extract defect features.Multi-dimensional features were effectively fused through principal component analysis,and they were mapped to an orthogonal basis feature space using coordinate transformation techniques,a two-dimensional terahertz image was reconstructed that contain rich feature information of insulator defects.Furthermore,the reconstructed image was input into the YOLOv3 based insulator defect detection model,where the Darknet-53 feature extraction network was used to capture defect features,achieving intelligent defect detection.The experimental results showed that the proposed method can effectively improve the quality of generated terahertz images of insulators and these images were rich in defect feature.The proposed method achieved a recognition rate consistently above 0.95 and could reach up to 0.98,demonstrating high detection accuracy.
As one of the important features of biometrics,fingerprints are widely used in criminal investigation.However,the traditional fingerprint acquisition technology is often limited to two-dimensional contact measurement,which is easy to be affected by finger humidity,force deformation and other factors,resulting in poor recognition effect.Therefore,for fingerprint measurement,this paper designs a small field of view imaging system based on structured light was.By adding lenses and other ways,the imaging field of structured light is reduced,and high-precision three-dimensional fingerprint information is obtained by non-contact measurement.At the same time,due to the influence of equipment accuracy,environmental noise and other factors,the acquired fingerprint point cloud usually contains noise.This paper analyzes the source of the acquired fingerprint point cloud noise,classifies the noise,proposes a multi-scale noise denoising method based on statistical analysis and Gaussian filter,and verifies its effectiveness through experiments.
Aiming at the problems of color distortion,low contrast and detail blur caused by light absorption and scattering in water,an underwater image enhancement algorithm based on red channel prior was proposed.Combining the traditional red channel prior theory with the generation adversarial network,a red channel prior module was designed to pay attention to the attenuation characteristics of the red channel in underwater images and conduct joint training with the encoder of the generator,so that the encoder could use the information provided by the red channel prior module to extract useful features better,which was conducive to solving the color bias problem of underwater images.The discriminator adopted global-local double discriminator,and constructed several loss functions to make the generated image consistent with the reference image in structure,content and color.Compared with other 10 algorithms,the UIEB,EUVP and LSUI public data sets obtained optimal or sub-optimal results.The enhanced underwater image peak signal-to-noise ratio(PSNR),structural similarity(SSIM),underwater image quality metric(UIQM),underwater color image quality assessment(UCIQE)and information entropy(IE)on the UIEB dataset were increased by 1.78 dB,0.073,0.016,0.004 and 0.02,respectively.Mean square error(MSE)decreased by 48.63.The algorithm proposed in this paper has achieved remarkable results in improving the quality of underwater images,showing obvious advantages in both subjective visual perception and objective data.
Phase-only hologram has been widely noticed in the field of holographic display due to the characteristics of having no conjugate scattered light and almost no optical loss in the imaging process.Computer-generated holograms based on deep learning have great potential for generating high-efficiency and high-quality display holograms,and have an important position in the field of phase-only hologram generation.This paper introduces computational holography technology,traditional phase-only hologram generation algorithms and model-driven deep learning fundamentals,overviews deep learning model-driven computational holography solutions based on deep learning proposed in recent years,compares optimization methods based on the network structure and loss function,and looks forward to the development and challenges of deep learning technology in the field of computational holography.
Aiming at the quality degradation problems such as low brightness and contrast,non-uniform illumination of images captured in real environments,a non-uniform low illumination image enhancement algorithm based on Retinex theory was proposed.Firstly,the original image was converted from RGB(red,green,blue)space to HSV(hue,saturation,value)space by the algorithm,and the V-component was extracted for enhancement processing.The Retinex decomposition was implemented by the algorithm through combined window filtering,with side window filtering and full window filtering being used for the edge and texture of the image respectively to obtain a locally smooth and structure-preserving illuminance component.For the illuminance component,a luminance transformation method based on the quadratic curve is used for indirect adjustment to effectively enhance the low illuminance and correct the non-uniform illuminance.For the reflection component,hybrid filtering was used to suppress noise and sharpen edge details.Finally,the enhanced illumination component and reflectance component were recombined to obtain the enhanced V-component,and then converted back to RGB space to obtain the final enhanced image.The experimentalresults show that the NIQE(natural image qualityevaluator),NIQMC(no-reference image quality metric),CEIQ(con-tent-enhanced image quality)and Entropy metrics for this method are 2.747,5.380,3.432 and 7.476,respectively,which are superior to most existing image enhancement algorithms.The proposed algorithm not only enhances the brightness and contrast,but also effectively corrects the non-uniformity of image illumination,making the visual effect clearer and texture details richer.
