Super-Resolution of Face Images Using Weighted Elastic Net Constrained Sparse Representation

High-resolution (HR) face images are usually preferred in many computer vision tasks. However, low-resolution (LR) face images, which are often obtained in real scenarios, can be converted to a high resolution with the super-resolution techniques. In this paper, we propose the weighted elastic net constrained sparse representation (WENSR) super resolution method for face images. The method considers image gradient and weighted elastic net penalties. Due to the high similarity between human faces, it is not very suitable to only use ℓ1-norm in the sparse representation model. The elastic net has a grouping effect and is more suitable for real-world data. The gradient is very important information in the image, we also use image gradient to enhance the final output. The tests of our method on both synthetic data and real-world data, such as FEI, CAS-PEAL-R1, and CMU+MIT face image dataset suggest a competitive performance gain in terms of peak signal to noise ratio (PSNR) and structural similarity (SSIM).