As the different parts of a single image appear in different scales, developing a deep learning based super resolution scheme that is capable of generating features at different scales and levels is essential. In this paper, a new residual block is proposed with a view of generating a rich set of features extracted at different scales and levels. It is shown through experimental results that the proposed scheme of designing the residual block results in a network that provides a superior performance with reduced number of parameters than that provided by the light-weight networks using other types of residual blocks.