ENHANCING AESTHETIC APPEAL OF PRINTS USING NEURAL NETWORKS - Amin, Parag; Baxi, Paramjit; Sidhu, Jaspreet; Lokhande, Suresh Kumar; Rastogi, Ritesh; Verma, Subhash Kumar (ShodhKosh: Journal of Visual and Performing Arts, 2025)
The appearance of printed pictures is extremely crucial to visual communication, product display, and art. Conventional methods of enhancing prints, such as colour repair, contrast control, and texturing refinements, tend to require that the end user manually adjusts the filter or relies on predefined filters, so they cannot be as adaptable to other types of pictures and printed media. With the emergence of deep learning and, in particular, neural networks, now, there are more options to enhance the appearance of printed materials using automated and effective methods. This study proposes a neural network-based algorithm to make printed photographs appear more attractive by learning complex, non-linear variants by using large datasets as the input. The process involves selecting the appropriate data sets, processing to balance the variations in texture, tone and colour and creating a convolutional neural network (CNN) framework that is actually designed to evaluate aesthetics. The model is trained to do perceptual loss functions and tested against standard picture improvement algorithms such as histogram equalisation and standard picture improvement algorithms. The experiments have better outcomes when compared to standard approaches in the aspects of colour balance, pattern accuracy, and overall visual appeal. In addition, the proposed model is highly effective with an expansive print media, including digital, letterpress, and cloth printing.