The effect of interpolation and super-resolution (SR) algorithms on quantitative and qualitative assessments of enlarged optical coherence tomography (OCT) images was investigated in this report. Spectral-domain OCT images from 30 eyes in 30 consecutive patients with diabetic macular edema (DME) and 20 healthy eyes in 20 consecutive volunteers were analyzed. Original image (OR) resolution was reduced by a factor of four. Images were then magnified by a factor of four with and without application of one of the following algorithms: bilinear (BL), bicubic (BC), Lanczos3 (LA), and SR. Differences in peak signal-to-noise ratio (PSNR), retinal nerve fiber layer (RNFL) thickness, photoreceptor layer status, and parallelism (reflects the complexity of photoreceptor layer alterations) were analyzed in each image type. The order of PSNRs from highest to lowest was SR > LA > BC > BL > non-processed enlarged images (NONE). The PSNR was statistically different in all groups. The NONE, BC, and LA images resulted in significantly thicker RNFL measurements than the OR image. In eyes with DME, the photoreceptor layer, which was hardly identifiable in NONE images, became detectable with algorithm application. However, OCT photoreceptor parameters were still assessed as more undetectable than in OR images. Parallelism was not statistically different in OR and NONE images, but other image groups had significantly higher parallelism than OR images. Our results indicated that interpolation and SR algorithms increased OCT image resolution. However, qualitative and quantitative assessments were influenced by algorithm use. Additionally, each algorithm affected the assessments differently.