Adaptive image superresolution for agrobased application

Superresolution is a concept to increase the resolution. The main objective of this paper is the study of iterative curvature based method for super-resolving low resolution of a leaf diseased images. The domain specific prior is incorporated into superresolution by the means of iterative curvature SR based estimation of missing high frequency details from infected leaf images. The model is composed of two step pixel filling approach. Through this proposed work, fine edges of SR images are preserved without applying complex mathematical algorithms based on wavelet, fast curvelet, etc. In this paper, we have validated proposed scheme over 9 infected leaf images of various crops like soybean, cotton, rose, citrus family etc. Shows better result in visual as well as subjective quality as that of complex multi frame SR algorithms like reconstruction and registration along with less computational time. This concept is most useful for agricultural expert for helping our farmers for exact leaf disease detection and accurate remedial actions The experimental result shows the best visible SR result of an infected leaf along with MSE and PSNR i.e. Statistical results. Also shows the comparison of proposed method with the existing techniques successfully.