Single Leaf Image Superresoution using NEDI Technique and Improved by PSO Algorithm

The concept of superresolution is for increasing the input image resolution. The paper focuses to study the iterative curvature based methods on single diseased leaf image having low resolution. For agricultural countries like India, Crop plays a vital role in country’s economic growth. Plants gets viral, bacterial or fungal diseases which have a significant reduction quantity as well as quality of agricultural products. The traditional approach of expert’s naked eye observation is time consuming. For this leaf identification system, leaf diseases detection system, plant diseases diagnosis system are developed which demands high resolution leaf images as input for better recognition rate. The use of superresolution technique which is related in both with the statistical relationship between high resolution output and low resolution input images and with the human perception of image quality can be considered as cheapest solution. However superresolution algorithms are being affected by artifacts such as over smoothed, jaggies, blurred or over sharped. The paper has described NEDI: New Edge Detection Interpolation with PSO Algorithm which outputs the superresolved image for a single leaf image with less memory requirement then NEDI or FCBI: Fast Curvature Based Interpolation. Fine edges in SR images are preserved without applying complex mathematical algorithms based on wavelet, fast curvelet etc. The concept can be useful for agricultural expert to help farmers for exact leaf disease detection and further accurate remedial actions for the same. The experimental result shows the best visible result of an infected leaf along with statistical comparing parameters: Mean Square Root (MSE) and Peak Signal to Noise Ratio (PSNR).