The single image super-resolution (SISR) is a computer vision task needed in many real-world applications. There are many methods developed to solve ill-posed SISR problem; however, these methods are based on attention mechanisms that need a large computing processing cost. So, these attention-based models cannot be used in real-world applications that need fast models. Thus, we propose an enhanced convolution mixer (EConvMixer) module to solve this SISR problem by using lower computing convolution layers. The EConvMixer is designed based on utilizing three convolution types, namely the dilated depthwise convolution for increasing the receptive field, the depthwise convolution for mixing spatial locations, and the pointwise convolution for mixing channel locations. Based on using this EConvMixer layer, we build a lightweight extended convolution mixer network (EConvMixN) for SR images …