A Supervised Learning Model for Wild Image Quality Enhancement Scheme Using Elevated Convolution and Contrast Enrichment Strategy

The rapid advancement of imaging technology and the proliferation of digital content have emphasised the importance of effective techniques for enhancing image quality, particularly in uncontrolled or challenging environments. This paper proposes a novel approach to improving the quality of “wild images”-images that are captured under unpredictable and diverse conditions-through the application of deep learning techniques. In order to evaluate the efficacy of the proposed scheme, we propose the Elevated Convolution and Contrast Enrichment Strategy (ECCES), a novel approach that is cross-validated with the conventional model, the Convolutional Neural Network (CNN). The architecture that has been proposed is specifically designed to address common quality issues, such as distortion, noise, and low resolution. In order to enhance the clarity, detail, and overall visual allure of images, our methodology implements a combination of perceptual loss functions, sophisticated network layers, and data augmentation techniques. We use a wide-ranging set of untamed images to test our model and see the quality boosts that outperform more significant comparisons to traditional enhancement techniques. The results presented here would show superior performance in subjective visual assessments while robust performance over a wide range of real-world scenarios. It would thus present an all-rounded analysis of the effectiveness of the model in relation to uses in high-quality image processing applications for complex inputs in various disciplines.