Image Blind Denoising Using a Generative Adversarial Network for LED Chip Visual Localization

Abstract

The noise in an LED chip image directly affects its visual positioning accuracy and efficiency. The existing denoising method can improve the image visual quality after removing the noise, but the improvement of the recognition rate and the positioning accuracy of the chip is not apparent. To solve this problem, this paper proposes a convolutional neural network combined with generative adversarial networks framework, the (CNN-GAN-GAN)-based blind denoiser, comprising three subnetworks. First, a noise extraction subnetwork with the proposed adaptive residual dense block extracts noise information from noisy images. Then, the noise modeling subnetwork with a dual-discriminator learns the noise distribution and constructs a paired training dataset. Finally, the blind denoising subnetwork with a hybrid loss function is trained for denoising. Contrast experiments show the proposed method can effectively suppress image noise and artifacts and meet the requirements of LED chip visual positioning accuracy while ensuring chip recognition rate.