Optimized Inverse Halftoning Technique Based on Bilateral Filter

Molina, J.; Ponomaryov, Volodymyr; Reyes-Reyes, Rogelio; Sadovnychiy, Sergiy; Cruz, Clara

doi:10.1109/tla.2019.8863178

Cited by 2 publications

(1 citation statement)

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“…In view of the characteristics of low pass filtering of human eyes, and high frequency characteristics of halftone dot-patterns, the earliest methods were based on low filters, such as the Gaussian filter, median filter or bilateral filter [31]. Low pass filtering simply removes halftone dot-patterns, but image details are also removed by this process.…”

Section: Traditional Inverse Halftoningmentioning

confidence: 99%

An Efficient Convolutional Neural Network Model Combined with Attention Mechanism for Inverse Halftoning

Shao

Zhang

2021

Electronics

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Inverse halftoning acting as a special image restoration problem is an ill-posed problem. Although it has been studied in the last several decades, the existing solutions can’t restore fine details and texture accurately from halftone images. Recently, the attention mechanism has shown its powerful effects in many fields, such as image processing, pattern recognition and computer vision. However, it has not yet been used in inverse halftoning. To better solve the problem of detail restoration of inverse halftoning, this paper proposes a simple yet effective deep learning model combined with the attention mechanism, which can better guide the network to remove noise dot-patterns and restore image details, and improve the network adaptation ability. The whole model is designed in an end-to-end manner, including feature extraction stage and reconstruction stage. In the feature extraction stage, halftone image features are extracted and halftone noises are removed. The reconstruction stage is employed to restore continuous-tone images by fusing the feature information extracted in the first stage and the output of the residual channel attention block. In this stage, the attention block is firstly introduced to the field of inverse halftoning, which can make the network focus on informative features and further enhance the discriminative ability of the network. In addition, a multi-stage loss function is proposed to accelerate the network optimization, which is conducive to better reconstruction of the global image. To demonstrate the generalization performance of the network for different types of halftone images, the experiment results confirm that the network can restore six different types of halftone image well. Furthermore, experimental results show that our method outperforms the state-of-the-art methods, especially in the restoration of details and textures.

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Section: Traditional Inverse Halftoningmentioning

confidence: 99%