A Multi-Scale Attention-Based Model for Image Enhancement and Classification

This paper presents EnhancedDetailNet, an advanced convolutional neural network meticulously crafted to address the intricate challenges of detail preservation and holistic comprehension inherent in the realm of image processing. Through the judicious incorporation of an all-encompassing image attention mechanism and a global feature encoding stratum, the model adeptly captures and amplifies pivotal image attributes, obviating the prerequisite for explicit image partitioning. By seamlessly amalgamating self-attention and multi-scale attention modules, EnhancedDetailNet acquires a nuanced appreciation of inter-scale feature interdependencies, judiciously elevating the salience of critical local regions and attributes. Moreover, the model accrues substantial benefits from the augmentation of its convolutional strata and the introduction of residual connections, thereby augmenting its aptitude for feature articulation and the expeditious propagation of gradients. Experimental assessments conducted on image classification and object detection benchmarks, leveraging the PlantVillage dataset, resoundingly underscore EnhancedDetailNet's supremacy in capturing intricate image nuances while concurrently curtailing parameter dimensions and preserving overarching features and semantic contexts. In comparative terms, EnhancedDetailNet emerges as the preeminent choice, outpacing traditional convolutional counterparts in the realms of detail retention and performance enhancement. Significantly, even when tailored to the same model dimensions as conventional approaches, EnhancedDetailNet persists in manifesting substantial performance strides, thus underscoring its preeminence in plant pathology image recognition endeavors. https://github.com/guo-mingyu/EnhancedDetailNet.git