  In Synthetic Aperture Radar (SAR) imaging, despeckling is very important for
image analysis,whereas speckle is known as a kind of multiplicative noise
caused by the coherent imaging system. During the past three decades, various
algorithms have been proposed to denoise the SAR image. Generally, the BM3D is
considered as the state of art technique to despeckle the speckle noise with
excellent performance. More recently, deep learning make a success in image
denoising and achieved a improvement over conventional method where large train
dataset is required. Unlike most of the images SAR image despeckling approach,
the proposed approach learns the speckle from corrupted images directly. In
this paper, the limited scale of dataset make a efficient exploration by using
convolutioal denoising autoencoder (C-DAE) to reconstruct the speckle-free SAR
images. Batch normalization strategy is integrated with C- DAE to speed up the
train time. Moreover, we compute image quality in standard metrics, PSNR and
SSIM. It is revealed that our approach perform well than some others.
