Using deep neural networks for image denoising in hardware-limited environments

Abstract

Image denoising remains a vital topic in digital image processing, as it aims to recover visually clear content from observations compromised by random fluctuations. This article provides an overview of advanced deep neural network methods for image denoising and compares their performance with classical techniques. Emphasis is placed on the capacity of modern deep architectures to learn data-driven relationships that preserve structural details more effectively than traditional strategies. Implementation is conducted in a programming environment using open-source libraries, and the research is carried out in a cloud-based platform with Google Colab to facilitate reproducible and scalable experimentation. Both classical and deep learning-based solutions undergo quantitative and visual assessment, measured through standardized quality indices such as signal-to-noise ratio and a measure of structural similarity, alongside processing speed analysis. Results indicate that neural network-based approaches deliver superior restoration accuracy and detail preservation, although they typically require more computational resources. Classical methods, while simpler to implement and often feasible on hardware with minimal capabilities, frequently struggle when noise levels are high or exhibit complex characteristics. Methods based on block matching and three-dimensional filtering achieve competitive outcomes but impose higher computational overhead, limiting their practicality for time-sensitive applications. Potential future directions include hybrid techniques that merge the benefits of convolutional and transformer-inspired frameworks, along with refined training methodologies that extend applicability to scenarios lacking large volumes of clean reference data. By addressing these challenges, the evolving field of image denoising stands to offer more efficient and robust solutions for diverse real-world tasks.