Optimisation-Based Image Grid Smoothing for SST Images

The present paper focuses on smoothing techniques for Sea Surface Temperature (SST) satellite images. Due to the non-uniformity of the noise in the image as well as their relatively low spatial resolution, automatic analysis on SST images usually gives poor results. This paper presents a new framework to smooth and enhance the information contained in the images. The gray levels in the image are filtered using a mesh smoothing technique called SOWA while a new technique for resolution enhancement, named grid smoothing, is introduced and applied to the SST images. Both techniques (SOWA and grid smoothing) represent an image using an oriented graph. In this framework, a quadratic criterion is defined according to the gray levels (SOWA) and the spatial coordinates of each pixel (grid smoothing) and minimised using non-linear programming. The two-steps enhancement method is tested on real SST images originated from Meteosat first generation satellite.