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Digital Image Processing And Machine Learning Research: Digital Color Halftoning, Printed Image Artifact Detection And Quality Assessment, And Image Denoising.
To begin with, we describe a project in which three screens for Cyan, Magenta, and Yellow colorants were designed jointly using the Direct Binary Search algorithm (DBS). The screen set generated by the algorithm can be used to halftone color images easily and quickly. The halftoning results demonstrate that by utilizing the screen sets, it is possible to obtain high-quality color halftone images while significantly reducing computational complexity.
Our next research focuses on defect detection and quality assessment of printed images. We measure and analyze macro-uniformity, banding, and color plane misregistration. For these three defects, we designed different pipelines for them and developed a series of digital image processing and computer vision algorithms for the purpose of quantifying and evaluating these printed image defects. Additionally, we conduct a human psychophysical experiment to collect perceptual assessments and use machine learning approaches to predict image quality scores based on human vision.
We study modern deep convolutional neural networks for image denoising and propose a network designed for AWGN image denoising. Our network removes the bias at each layer to achieve the benefits of scaling invariant network; additionally, it implements a mix loss function to boost performance. We train and evaluate our denoising results using PSNR, SSIM, and LPIPS, and demonstrate that our results achieve impressive performance on both objective and subjective IQA assessments.