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All Journal International Journal of Advances in Applied Sciences
Suad Khairi Mohammed
University of Technology
Earth & Planetary Sciences Environmental Science Materials Science & Nanotechnology Mathematics Physics

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A new generation of artificial intelligence contributing to improving the image quality Salwa A. Alagha; Hadeel N. Abdullah; Suad Khairi Mohammed
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp664-676

Abstract

High-resolution (HR) images provide inclusive and critical information, which is substantial for many implementations. Production operation for high-quality images from low-quality images can be costly and time consuming. The main advancement in this domain is produced by enhanced super-resolution generative adversarial network (ESRGAN); the ESRGAN and different deep learning (DL) models exhibit prominent advances in image super-quality. This research proposes introducing the discrete wavelet transform (DWT) as a multi-scale analysis stage that feeds into the network, whereby the frequencies are analyzed before being fed into the generative adversarial networks (GAN). The goal is to enhance the ability to recover edges and fine details, especially in low-resolution images. The performance of this proposed model is implemented, evaluated, and comparatively assessed. Key performance parameters, such as peak signal-to-noise ratio (PSNR) and structural similarity index metric (SSIM), are calculated, which compare the proposed model with other image-improving models (Bicubic, SRResNet, and ESRGAN). The experimental results indicate that the proposed method ESRGAN new yields a good result in image improvement, with a PSNR of (26.22, 26.00, 25.51, and 23.89) and an SSIM of (0.6638, 0.6255, 0.5882, and 0.6286) for four datasets, respectively.
Increasing the efficiency of deep learning performance using adaptive filters Suad Khairi Mohammed; Sabah A. Gitaffa; Reem I. Dawai
International Journal of Advances in Applied Sciences Vol 15, No 2: June 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijaas.v15.i2.pp775-789

Abstract

Deep learning algorithms have become one of the most important innovative technologies that have entered almost all areas of life. These technologies perform complex operations and deal with huge data sets. One of the benefits of deep learning is the inherent flexibility in developing approximate estimates for vast and diverse data sets. Data scientists can develop approximate estimates of almost anything using deep learning and neural networks. The main challenges in deep learning include the problem of data quality and quantity while ensuring large, diverse, and high-quality datasets. It also suffers from the problem of providing computational resources due to the high demand for powerful devices, such as processing and memory units. Additionally, it suffers from the problem of interpretability of the case due to difficulty in understanding and explaining typical decisions. This study proposes an innovative method to reduce these problems in the working mechanisms of deep learning algorithms by merging their layers and hybridizing them using adaptive digital filters. These filters help provide devices for efficient resources and memory units, in addition to the capabilities of analyzing and interpreting various states of processing unit availability. In this study, models of hybrid deep learning techniques with adaptive digital filters were designed and implemented, obtaining good results in reducing training error rates, improving the efficiency of outputs, and reducing the computational effort to high levels.
Co-Authors Hadeel N. Abdullah Reem I. Dawai Sabah A. Gitaffa Salwa A. Alagha