A novel adaptive image zooming scheme via weighted least-squares estimation

A critical issue in image interpolation is preserving edge detail and texture information in images when zooming. In this paper, we propose a novel adaptive image zooming algorithm using weighted least-square estimation that can achieve arbitrary integer-ratio zoom （WLS-AIZ） For a given zooming ratio n, every pixel in a low-resolution （LR） image is associated with an n x n block of high-resolution （HR） pixels in the HR image. In WLS-AIZ, the LR image is interpolated using the bilinear method in advance. Model parameters of every n×n block are worked out through weighted least-square estimation. Subsequently, each pixel in the n × n block is substituted by a combination of its eight neighboring HR pixels using estimated parameters. Finally, a refinement strategy is adopted to obtain the ultimate HR pixel values. The proposed algorithm has significant adaptability to local image structure. Extensive experiments comparing WLS-AIZ with other state of the art image zooming methods demonstrate the superiority of WLS-AIZ. In terms of peak signal to noise ratio （PSNR）, structural similarity index （SSIM） and feature similarity index （FSIM）, WLS-AIZ produces better results than all other image integer-ratio zoom algorithms.

Surface Weather Parameters Forecasting Using Analog Ensemble Method over the Main Airports of Morocco

Surface weather parameters detain high socioeconomic impact and strategic insights for all users,in all domains(aviation,marine traffic,agriculture,etc.).However,those parameters were mainly predicted by using deterministic numerical weather prediction(NWP)models that include a wealth of uncertainties.The purpose of this study is to contribute in improving low-cost computationally ensemble forecasting of those parameters using analog ensemble method(AnEn)and comparing it to the operational mesoscale deterministic model(AROME)all over the main airports of Morocco using 5-yr period(2016-2020)of hourly datasets.An analog for a given station and forecast lead time is a past prediction,from the same model that has similar values for selected predictors of the current model forecast.Best analogs verifying observations form AnEn ensemble members.To picture seasonal dependency,two configurations were set;a basic configuration where analogs may come from any past date and a restricted configuration where analogs should belong to a day window around the target forecast.Furthermore,a new predictors weighting strategy is developed by using machine learning techniques(linear regression,random forest,and XGBoost).This approach is expected to accomplish both the selection of relevant predictors as well as finding their optimal weights,and hence preserve physical meaning and correlations of the used weather variables.Results analysis shows that the developed AnEn system exhibits a good statistical consistency and it significantly improves the deterministic forecast performance temporally and spatially by up to 50%for Bias(mean error)and 30%for RMSE(root-mean-square error)at most of the airports.This improvement varies as a function of lead times and seasons compared to the AROME model and to the basic AnEn configuration.The results show also that AnEn performance is geographically dependent where a slight worsening is found for some airports.