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

Abstract 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.