Description

Enhancement of the images will be more helpful in surveillances and remote sensing applications. While increasing the size of the image the original image quality will be affected. In order to avoid the loss of quality while enhancing the image, Non Local Means (NLM) Optimized Sparse Method is used in this paper. The noises and the pixel differences occurring in the up sampling and down sampling of the images were identified and they were removed based on the proposed method. The performance of the proposed method is proved using the performance parameters. The main objective of the process is to increase the resolution of the input image. To handle the noises occurring due to the up sampling and down sampling process, optimization methods were used. To identify recurring noise pixels, sparse model is included. To handle the noises, convex minimization process is used. The noise occurrence in the images were verified based on the calculated performance metrics. Sparse representation of signals on over-complete dictionaries is a rapidly evolving field. The basic model suggests that natural signals can be compactly expressed as a linear combination of pre-specified atom signals, where the linear coefficients are sparse. This property is utilized in both the dictionary learning and the sparse coding process to capture more structural details for the reconstructed image. Apart from a general dictionary, example patches from the salient regions are extracted to train a salient dictionary. We also incorporate context-aware sparse decomposition to model dependencies between dictionary atoms of adjacent patches, especially in the salient regions. The performance metrics used in the proposed methods were SSIM and PSNR.