MWformer: a novel low computational cost image restoration algorithm

The development of the Internet of Things has led to a surge in edge devices. The image detection algorithm, one of the commonly used algorithms in edge computing, is affected by environments such as weather, light, air humidity, smoke, and dust, so an image restoration algorithm is needed to preprocess the image in practice. Most currently proposed deep learning image restoration algorithms are based on general-purpose servers with high computational overhead to minimize the environmental effects. Edge devices are limited in size, power consumption, and computing performance, making the performance of deep learning-based image restoration algorithms on edge devices poor. In this work, we propose an image restoration algorithm that combines wavelet transform and transformer, named MWformer, to reduce computational overhead, optimize the feature map size, network structure, and network depth, and introduce the wavelet transformation to reduce the super-parameters. Experimental tests on multiple public datasets for various image restoration tasks show that the proposed MWformer ensures high performance in numerous image restoration tasks, and the computational overhead is 10% of the state-of-the-art algorithm, on average.