Prior Guided Multiscale Dynamic Deblurring Network for Diffraction Image Restoration in Droplet Measurement

High-precision measurement of micrometer-scale flying droplets is demanded in inkjet printing manufacturing. However, the measurement accuracy is limited by droplet image degradation caused by optical diffraction and actual imaging conditions. Most existing image restoration methods focus on defocus blur and motion blur and pay less attention to diffraction degradation, which cannot handle real-world complex degradation well. In this study, to address the challenges in droplet image restoration, we propose a diffraction-Gaussian degradation framework to simulate actual degradation and a prior guided multiscale dynamic deblurring network (PDDN) for image restoration. PDDN explicitly utilizes degradation prior information with the proposed fast Fourier transform (FFT)-based prior extraction (FPE) module and the multiscale dynamic deblurring (MSDD) module. FPE extracts the degradation prior with the combination of Weiner deconvolution and deep learning. MSDD restores intermediate features using kernel prediction-based dynamic convolution under the guidance of the learned prior. PDDN employs a U-shaped Transformer architecture along with prior guided dynamic deblurring to achieve nonblind deblurring. Experiments on four synthesized datasets demonstrate that PDDN achieves state-of-the-art performance in diffraction image restoration. The effectiveness of the degradation framework and PDDN is proved in real-world image restoration, with droplet measurement accuracy improved from 3% to 2.42%.

I. Introduction

Inkjet printing is considered one of the most promising technologies in organic light-emitting diode (OLED) [1] manufacturing, where flying droplet measurement is the key to achieving a high yield rate. Online visual measurement has been widely employed in droplet measurement due to its accuracy and reliability. However, limited by optical diffraction, general visual measurement methods can hardly achieve accuracy better than ±3%. Diffraction blur caused by the optical resolution limit of lenses is inevitable and extremely affects image quality in high magnification vision systems. As shown in Fig. 1, droplet images captured by the online vision system suffer from severe blur with terrible gray distribution. Printing defects and Mura defects [2] often occur without accurate measurement of droplets. Toward Mura-free manufacturing of OLED, we have developed a series of inkjet printing equipment along with measurement and control technologies [3], [4], [5]. How to break through the bottleneck of droplet visual measurement remains challenging and is worth studying. Fig. 1.

Diffraction blur in droplet measurement. (a) Inkjet printing. (b) Blurred droplet image. (c) Printing defects caused by abnormal droplet volume. (d) Gray distribution of (b).