Set5

Set5 is a small benchmark dataset consisting of five standard grayscale images that have been widely used in the field of image processing, particularly in early single-image super-resolution (SR) research. The collection was introduced as a concise testbed that allows researchers to compare the effectiveness of SR algorithms, with results typically reported in terms of peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).

Although the exact five images can vary slightly between sources, Set5 traditionally includes a mix of natural scenes and textures designed to challenge algorithms with both smooth regions and high-frequency detail. The dataset's small size makes it convenient for rapid experimentation and development, but it also limits its statistical representativeness compared with larger collections introduced later, such as Set14, BSD100, and Urban100.

Because Set5 has been part of the literature for many years, it remains a common point of reference in historical surveys and tutorials, particularly in discussions of early interpolation baselines and the progression of SR methods from classical priors to modern learning-based approaches. The five-image benchmark is often used in conjunction with standardized downsampling and blur kernels to facilitate fair comparisons.

Today, Set5 is primarily of archival and historical interest, with contemporary SR research favoring larger, more diverse datasets for robust evaluation.