Salt-and-pepper denoising based on lightweight convolutional neural networks for flexible AMOLED

To improve the performance of the image preprocessing module in consumer electronics using an active-matrix organic light-emitting diode display panel, the concept of judging before processing for salt-and-pepper denoising is originally proposed. Firstly, a dataset for salt-and-pepper noise image classification is constructed, and a convolutional neural network (CNN) for judging noise image (CNN-J) is trained. Image classified as normal by CNN-J is not processed, while the classified noisy image is denoised. In the denoising process, a marking image and a rough denoised image are generated by CNN for noise mask (CNN-M) and CNN for denoising (CNN-D), respectively. Subsequently, the refined denoised image is output using the proposed refining mechanism. The middle layers of CNN-M and CNN-D are constructed by depth-separable CNN to reduce the network complexity. Experimental results show that the misjudging rate of CNN-M marking is reduced by 19.94% compared with the best existing marking method. Compared with the traditional methods, the peak signal to noise ratio of the proposed method is increased by 2.95% and the information loss is reduced by 21.46%. In addition, the computational complexity is at least 11.18% lower than that of the traditional CNN. Finally, the display of salt-and-pepper denoised images on the flexible AMOLED is realized.