A real time differentiation between generative adversarial network v3 and enhanced super resolution generative adversarial networks in blind face image restoration to improve naturalness image quality evaluator score - Harish, M. K.; Jaisharma, K.; Narendran, R. (AIP conference proceedings, 2024)
The photos taken by all, bring backs remarkable memories for everyone life. By keeping in mind, this research was focused to enhance the quality of an image, aiming to bring it as close to its original high-quality state. Advanced image processing techniques are employed to achieve this goal. This approach is particularly useful when dealing with degraded images that remain usable but require improvement to enhance their utility or presentation. To elevate the Naturalness Image Quality Evaluator (NIQE) score, we employ the Novel Generative Adversarial Network v3 (NGAN3) with Enhanced Super Resolution Generative Adversarial Networks (ESRGAN). Our study involved two distinct groups: Group 1, which utilized the NGAN3 algorithm, and Group 2, which employed the ESRGAN algorithm. The group sample size carefully determined using the Clincalc tool. This tool also facilitated the calculation of error rates, including an beta level of 0.2, alpha level of 0.05 with power of 0.05. In total, we analyzed 40 samples (20 per group). The NGAN3 algorithm yielded an average NIQE score of 4.46, while the ESRGAN algorithm produced an average NIQE score of 6.36. Notably, the ESRGAN algorithm demonstrated statistical significance with a significance value (P) of 0.001, as determined by a sample t-test. This result underscores the superiority of NGAN3 in terms of image quality. Furthermore, Novel GAN3 generates visually compelling and realistic images, surpassing the existing algorithm. Consequently, it holds the potential to enhance employment prospects by increasing the likelihood of securing well-paid jobs.