Low-resolution MRI images in the through-plane direction impose constraints on both time and cost. This insufficient level of resolution does not meet the requirements for high-resolution imaging that is necessary for early diagnosis of brain diseases and morphometric analysis. Conventional single-image super-resolution (SISR) techniques have significant obstacles, such as effectively integrating intricate local details with the overall anatomical structure. In order to address these difficulties, we propose a novel technique known as the Progressively Serial-Stacked Encoder Decoder (PSSED) to improve the resolution of brain MRI scans. This approach employs convolutional blocks to extract specific information and enhance the quality of the image. Our strategy has been proven to be superior to previous Single Image Super-Resolution (SISR) methods based on extensive experimentation using publically available datasets. Our findings highlight the potential of deep learning techniques to address the constraints of low-resolution MRI images, paving the path for improved diagnostic accuracy and lower radiation exposure in clinical practice.