Robust hybrid normalized convolution and forward error correction in image reconstruction

Frequency domain Normalized Convolution (NC) process is widely performed on images to retrieve and extract valuable information in noisy and distorted environment. Genetic Normalized Convolution (GNC) is carried out for features extraction in an image or features reconstructions in a distorted image. In this paper a hybrid approach is adopted where robust algorithm of convolution based on Normalized Convolution and Genetic Normalized Convolution (GNC) is implemented and performed on a noisy image to reconstruct the original image. Unlike in Normalized Convolution (NC) where it is done at specific positions. Thus random behavior of sampling process is catered in robust Hybrid Normalized Convolution due to the involvement of random sampling pixels criteria and then forward Error Correction, it gives local optimum results in image reconstruction. In robust Hybrid Normalized Convolution approach samples are chosen based on their importance, criteria measured by Phase Congruency and Radial Symmetries algorithms. In the end robust NC and Forward Error Correction analysis is performed and suggested to improve and ease the reconstruction while avoiding data losses and storing less sample in an image finally reaches an local optima.