Texture preserving super resolution in thermal images

This paper proposes a new super-resolution (SR) image reconstruction method targeting edges and textured regions in thermal images. Proposed two stage method runs two Bayesian SR estimators at the first stage. These estimators are; maximum likelihood method and maximum a-posteriori method with Tikhonov type regularization. The piksel-to-piksel difference image of these two estimates is an high frequency (HF) image including observation noise, process noise, edges and textures (that are smoothed out by regularizers). In the second stage of the proposed method, the difference image is post-processed to extract edge and texture information while eliminating noise. The proposed method uses a Gabor filter family to analyze this difference image at various frequencies and directions. The strong frequency components are restored and added to the MAP estimate to obtain the final image. The proposed methods are validated through simulations on several textured surfaces from Brodatz data base. Peak-signal-to-noise ratio (PSNR) measures and illustrations clearly shows the success of the proposed method. The Real experiments on uncooled thermal cameras are also conducted to compare the methods to classical SR methods known to literature.