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The recent outbreak of COVID-19 has motivated researchers to contribute inthe area of medical imaging using artificial intelligence and deep learning.Super-resolution (SR), in the past few years, has produced remarkable resultsusing deep learning methods. The ability of deep learning methods to learn thenon-linear mapping from low-resolution (LR) images to their correspondinghigh-resolution (HR) images leads to compelling results for SR in diverse areasof research. In this paper, we propose a deep learning based imagesuper-resolution architecture in Tchebichef transform domain. This is achievedby integrating a transform layer into the proposed architecture through acustomized Tchebichef convolutional layer ($TCL$). The role of TCL is toconvert the LR image from the spatial domain to the orthogonal transform domainusing Tchebichef basis functions. The inversion of the aforementionedtransformation is achieved using another layer known as the Inverse Tchebichefconvolutional Layer (ITCL), which converts back the LR images from thetransform domain to the spatial domain. It has been observed that using theTchebichef transform domain for the task of SR takes the advantage of high andlow-frequency representation of images that makes the task of super-resolutionsimplified. We, further, introduce transfer learning approach to enhance thequality of Covid based medical images. It is shown that our architectureenhances the quality of X-ray and CT images of COVID-19, providing a betterimage quality that helps in clinical diagnosis. Experimental results obtainedusing the proposed Tchebichef transform domain super-resolution (TTDSR)architecture provides competitive results when compared with most of the deeplearning methods employed using a fewer number of trainable parameters.

Tchebichef Transform Domain-based Deep Learning Architecture for Image Super-resolution