I agree with Santhosh. Frequency filtering is used before enhancement, but there are no specific filters present that can enhance that image.

Quote Richard : "??? image enhancements are not filters...".

Quote Sandeep : "there are no specific filters present that can enhance that image".

Why not? Maybe only semantics, but to me, image enhancement is ANY operation that improves the image quality for a specific purpose. A convolution filter to reduce noise (blur or median, like Santhosh suggested) is 'image enhancement'. Correct me if I'm wrong...

In remote sensing the main problem is not blur, it is improper Orthorectification and Radiometric resolution. Here a filter like Spatial filter can be used.

@Richard: Then the question becomes: is 'image enhancement' an agreed-upon term?

In a relatively young science like image-processing, such a question is hard to answer; terms need time to settle, some stick around, others become obsolete. Some should have been deleted a long time ago (e.g. centrifugal force) but keep popping up in text-books anyway...

MathWorks says:

http://www.mathworks.es/discovery/image-enhancement.html

Image enhancement is the process of adjusting digital images so that the results are more suitable for display or further image analysis. For example, you can remove noise, sharpen, or brighten an image, making it easier to identify key features.

Here are some useful examples and methods of image enhancement:

Filtering with morphological operators

Histogram equalization

Noise removal using a Wiener filter

Linear contrast adjustment

Median filtering

Unsharp mask filtering

Contrast-limited adaptive histogram equalization (CLAHE)

Decorrelation stretch

I will not claim that matlab is authoritative, but while searching with Google I did not come across anything saying that image enhancement does not change pixelvalues.

A median filter is indeed not a convolution filter, oops...

Handbook of Image and Video Processing (page 73, 1st edition 2000, Al Bovik) says:

The term "Image enhancement” has been widely used in the past to describe any operation that improves image quality by some criteria. However, in recent years, the meaning of the term has evolved to denote image-preserving noise smoothing. This primarily serves to distinguish it from similar-sounding terms, such as “image restoration” and “image reconstruction”, which also have taken specific meanings.

these images are usually degradated by speckle noise. This is multiplicative noise.

see

1) C.P. Loizou, C.S. Pattichis, “Despeckle filtering algorithms and software for ultrasound imaging,” Synthesis Lectures on Algorithms and Software for Engineering, Ed. Morgan & Claypool Publishers, 1537 Fourth Street, Suite 228, San Rafael, CA 94901 USA, June 2008, ISBN-13: 9781598296204.

2) C.P. Loizou, C.S. Pattichis, M. Pantziaris, T. Tyllis, A. Nicolaides, “Quality evaluation of ultrasound imaging in the carotid artery based on normalization and speckle reduction filtering,” Med. Biol. Eng. Comput.,” vol. 44, no. 5, pp. 414-426, 2006.

3) C.P. Loizou, C.S. Pattichis, C.I. Christodoulou, R.S.H. Istepanian, M. Pantziaris, A. Nicolaides “Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery,” IEEE Trans. Ultras. Ferroel. Freq. Contr., vol. 52, no. 10, pp. 1653-1669, 2005.

4) C.P. Loizou, C. Theofanous, M. Pantziaris, T. Kasparis, “Despeckle filtering software toolbox for ultrasound imaging of the common carotid artery”, Comput. Meth. & Progr. Biomed., vol. 114, pp. 109-124, 2014

@Lambert. thank you very much ...

and thanks to all contributors