Abstract:
Handloom design creation, deeply rooted in cultural heritage, has traditionally relied on manual craftsmanship. The individual
minds are conditioned to biased and coming up with combine aesthetics of non-handloom designs with handloom designs is a tough
task. This paper explores an innovative approach by fusing deep convolutional neural networks with cellular automata for generating
handloom designs to automate and enhance this intricate process. Further the output is processed with a higher resolution network. The
fusion network works on higher-levels of feature pyramid, managing the image layout at a texture level. We implemented the approach
with different weight ratios to generate the outcomes. This method also avert over-excitation artifacts and reduces implausible feature
mixtures in compare to previous approaches. It allows to generate adoptable result with increased visual effects. Unlike existing methods,
the combined system can match and fit local features with considerable variability and yielding results. The outcomes shows potential
of this fusion in pushing the boundaries of design innovation in the field of handloom textiles. Qualitative and quantitative experiments
demonstrate the superiority of the introduced method among all other existing approaches. The work established a comprehensive
benchmark for comparision and results into a new publicly accessible “HandloomGCN” dataset of handloom clothes for this research
field.
