Image Resolution Increasing using Segmentation

Increasing the image resolution is very important and is used in various fields such as medicine, photography, and machine vision. It is possible to see more details of the image and analyze it better by increasing the image resolution. However, increasing the image resolution has also been associated with some challenges. Increase in noise, increase in artificial details, and high processing time are among the typical challenges. In addition, interaction with image complexities such as images with repetitive patterns and non-textured noises creates other challenges either. Image is divided into smaller parts using segmentation. Then, the images are combined with each other using the support vector regression algorithm and new images are created. A multi-stage process has been used to increase the image quality in this research and the pre-processing operation has been carried out in the first stage in order to improve the image quality. Three phases of training, testing, and parameter adjustment have been used after the pre-processing operation in order to increase the image quality. In the training section, the images are first converted to lower levels, and the color segmentation operation takes place at the lower levels. After the image classification operation in terms of color, the gradient is used to extract the image properties. The support vector regression algorithm was used to predict the image pixels, and this algorithm was improved by a meta-heuristic algorithm called whale algorithm. Evaluation parameters including PSNR and SSIM criteria have been used in this research that yielded promising results.