A new face reconstruction technique for noisy low-resolution images using regression learning. (April 2023)

• Record Type: Journal Article
• Authors: Rai, Deepak; Rajput, Shyam Singh
• Is Part Of: Computers & electrical engineering. Volume 107(2023)
• Publication Date: 2023-04
• Subjects: Position-patch -- Face super-resolution -- Face reconstruction -- Gaussian noise -- Face hallucination -- Regression learning -- Gaussian process regression; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2023.108642
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: Over the past few decades, there has been a lot of advancement in face hallucination (or super-resolution) technologies, with the position-patch-based locality constrained methods showing the most promising performance. However, these methods have not achieved up to the mark performance with noisy images as they reconstructed the high-resolution images without knowing the actual level of noise in the input LR faces. To this end, this paper proposes a Regression Learning based Locality constrained Representation (RLLcR) technique for noise robust face hallucination. In RLLcR, the Gaussian process regression learning approach and locality-constrained representation approach are integrated together to super resolve the noisy low-resolution faces. Specifically, a Gaussian process regression learning based noise level ( G P R N L ) prediction model is introduced that predicts the level of noise in the test images. Further, the predicted noise level is incorporated into the objective function of the proposed RLLcR technique to regularize the reconstruction error. These contributions make the proposed method robust to noise. The experiments are performed on FEI and CAS+PEAL publicly available face datasets and some real-world captured face images to demonstrate the performance of the proposed RLLcR technique. The results show improvement of 17%, 15%, 17%, 6%, 72%, 38%, 26%, 11%, and 35% on FEI dataset; 15%, 10%, 8%, 5%, 76%, 20%, 23%, 13%, and 26% on CAS+PEAL dataset in terms of PSNR, SSIM, MS-SSIM, FSIM, IFC, UQI, D-SSIM, NLPD, and BRISQUE respectively over the best compared method reported in the literature. Moreover the visual results on real-world images also reveal the superiority of the proposed RLLcR technique.