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In this paper we will examine the effect of different parameters in the quality of real compressively sampled images in the compressed sensing framework. We will select a variety of different real images of different types and test the quality of the recovered images, the recovery time, and required resources when different measurement methods with different parameters are used or when different recovering methods are applied. Then we will propose an algorithm to reduce the noise in the recovered images and sharpen them simultaneously. The algorithm exploits a well-known bilateral filtering in order to increase the confidence in margins and edges, and then uses an adaptive unsharp mask method to sharpen the images. The adaptive unsharp mask method extends the ordinary unsharp mask method and uses machine learning square loss minimization and regression in order to learn the optimal unsharping parameters. We will argue why both bilateral filtering and unsharp mask methods should be used in the algorithm simultaneously. Finally, we will show the results of applying the algorithm on real images that are recovered using the compressed sensing method and we will interpret the experimental results.