Set_41_img_29_491615812a62cdcd56c55b1e29d25768.jpg May 2026
Specifically, this image corresponds to the image, which is the 29th image (IMG_29) in the expanded or internal numbering of the Set14 collection. The paper that originally introduced and popularized this dataset for super-resolution evaluation is:
On Single Image Scale-Up Using Sparse-Representations of Raw Image Patches Authors: Roman Zeyde, Michael Elad, and Matan Protter Set_41_IMG_29_491615812a62cdcd56c55b1e29d25768.jpg
The image filename is a specific identifier from the Set14 dataset , a widely used benchmark in Single Image Super-Resolution (SISR) research. Specifically, this image corresponds to the image, which
The long alphanumeric string ( 491615812a62cdcd... ) is a hash typically generated by automated dataset loaders (like those in PyTorch or TensorFlow environments) or specific repositories on platforms like Kaggle or GitHub to ensure file integrity. ) is a hash typically generated by automated
While often referred to as Set14 , it is an extension of the earlier Set5 dataset. It consists of 14 images with varying textures and structures used to test how well algorithms can upscale images while preserving sharp edges.
Lecture Notes in Computer Science (LNCS) , 2010 (Presented at the 7th International Conference on Curves and Surfaces). Context and Usage
You will see this image and its associated paper cited in almost every major Super-Resolution study, including those for SRCNN , VDSR , and EDSR , as it serves as a standard for comparing Peak Signal-to-Noise Ratio ( PSNR ) and Structural Similarity Index ( SSIM ) values. AI responses may include mistakes. Learn more