Adaptive Style Transfer Using SISR

Style transfer is the process that aims to recreate a given image (target image) with the style of another image (style image). In this work, a new style transfer scheme is proposed that uses a single-image super resolution (SISR) network to increase the resolution of the given target image as well as the style image and perform the transformation process using the pre-trained VGG19 model. The Combination of perceptual loss and total variation loss is used which results in more photo-realistic output. With the change in content weight, the output image contains different semantic information and precise structure of the target image resulting in visually distinguishable results. The generated outputs can be altered accordingly by the user from artistic style to photo-realistic style by changing the weights. Detailed experimentation is done with different target image and style image pairs. The subjective quality of the stylised images is measured. Experimental results show that the quality of the generated image is better than the state of the art existing schemes. This proposed scheme preserves more information from the target image and creates less distortion for all combinations of different types of images. For more effective comparison, the contour of the stylizing images are extracted and also similarity is measured. This experiment shows that the result images have contour closer to the target images, also measured similarity is found maximum which indicates more preservation of semantic information than other existing schemes.