COVID-19 is a brand new pulmonary illness which is driving stress to the hospitals as a result of massive variety of circumstances worldwide. Imaging of lungs can play a key position within the monitoring of well being standing. Non-contrast chest computed tomography (CT) has been used for this function, primarily in China, with important success. Nonetheless, this strategy can’t be massively used, primarily for each excessive danger and value, additionally in some international locations, this instrument shouldn’t be extensively accessible. Alternatively, chest X-ray, though much less delicate than CT-scan, can present essential details about the evolution of pulmonary involvement through the illness; this facet is essential to confirm the response of a affected person to therapies. Right here, we present enhance the sensitivity of chest X-ray by way of a nonlinear post-processing instrument, named PACE (Pipeline for Superior Distinction Enhancement), combining correctly Quick and Adaptive Bidimensional Empirical Mode Decomposition (FABEMD) and Distinction Restricted Adaptive Histogram Equalization (CLAHE). The outcomes present an enhancement of the picture distinction as confirmed by three extensively used metrics: (i) distinction enchancment index, (ii) entropy, and (iii) measure of enhancement. This enchancment provides rise to a detectability of extra lung lesions as recognized by two radiologists, who evaluated the photographs individually, and confirmed by CT-scans. The outcomes present this technique is a versatile and an efficient strategy for medical picture enhancement and can be utilized as a post-processing instrument for medical picture understanding and evaluation.