# A Multi-Scale Unsupervised Feature Extraction Network with Structured Layer-Wise Decomposition
## 1 Introduction
Collectively, these studies demonstrate the breadth and versatility of multi-scale feature extraction, highlighting its transformative impact across disciplines such as medical imaging, geospatial analysis, speech recognition, and image processing. Building on these foundations, this study introduces a novel, unsupervised feature extraction framework based on autoencoders to construct multi-scale representations from grayscale image data. The proposed model decomposes each input into three distinct components:  
* A smooth layer that captures coarse structural features,
* A detail layer preserving fine textures and high-frequency information,
* A residual layer isolating remaining, often less informative, content. This decomposition is achieved through an encoding stage using convolutional filters, followed by three parallel decoding branches--each responsible for reconstructing one of the components. To ensure each branch captures distinct, non-overlapping features, dedicated variational loss functions are applied independently. The reconstructed components are then summed to recover the original image, thereby validating the effectiveness of the decomposition strategy. A key advantage of this approach lies in its fully unsupervised training paradigm, which removes the dependency on labeled data. Moreover, the model enhances grayscale input by generating expressive three-channel outputs, which are more compatible with existing pre-trained convolutional networks requiring three channel input. In contrast to many task-specific feature extractors embedded within custom model architectures, the proposed framework operates as a general-purpose preprocessing step. It can be seamlessly integrated with a wide array of downstream machine learning modelsand tasks, providing richer, multi-scale representations without necessitating architectural changes. In summary, this study offers the following key contributions:  
* Introduction of a novel unsupervised feature extraction framework based on an autoencoder structure that decomposes input data into smooth, detailed, and residual layers without requiring labeled data. * Design of a multi-branch autoencoder architecture that enables transforming single-channel inputs into rich, multi-scale representations to enhance machine learning task performance. * Implementation of a layer-specific variational loss strategy to preserve semantic consistency across decomposed components while ensuring accurate reconstruction of the original input. * Demonstration that the proposed method can be used as a flexible preprocessing step for various models, particularly pre-trained networks that require three-channel input. We organized the rest of the paper as follows. We provide a comprehensive description of the proposed method in Section 2. Section 3 outlines the experimental setup along with the datasets used for the evaluation and presents and analyzes the experimental results, highlighting the effectiveness of the proposed approach. Section 4 discusses the experimental results of the proposed method. Section 5 concludes the study by summarizing the key findings and offering directions for future research.