Requirements and Implementation Challenges of Deep Learning in Digital Image Processing: A Literature Review

Abstract

The rapid growth of visual data across various sectors has increased the demand for efficient and intelligent digital image processing techniques. Deep learning has emerged as a dominant approach due to its ability to automatically learn complex visual representations from large-scale datasets. This study presents a literature review of the requirements and implementation challenges of deep learning in digital image processing. The review was conducted by analyzing recent scientific publications published between 2022 and 2026 from major academic databases. The study examines key implementation requirements, including datasets, annotation processes, image preprocessing, model architectures, computational infrastructure, performance evaluation, data security, and deployment environments. Furthermore, the review identifies major implementation challenges, such as overfitting, limited data availability, high computational costs, model interpretability issues, and privacy concerns. The findings indicate that successful deep learning implementation depends not only on advanced architectures but also on the quality of data, availability of computational resources, appropriate evaluation strategies, and ethical considerations. This study contributes a structured analytical framework that can support future research and practical implementation of deep learning in digital image processing.