# A Novel Cost Function for Despeckling using Convolutional Neural Networks

## TL;DR

This paper introduces a new convolutional neural network-based despeckling method for SAR images, utilizing a novel cost function that considers spatial consistency and noise statistics to improve image clarity.

## Contribution

It proposes a new cost function for CNN-based despeckling that enhances SAR image quality by integrating spatial and statistical noise properties.

## Key findings

- Improved despeckling performance on simulated SAR data.
- Effective preservation of image details and structures.
- Enhanced noise reduction compared to traditional methods.

## Abstract

Removing speckle noise from SAR images is still an open issue. It is well know that the interpretation of SAR images is very challenging and despeckling algorithms are necessary to improve the ability of extracting information. An urban environment makes this task more heavy due to different structures and to different objects scale. Following the recent spread of deep learning methods related to several remote sensing applications, in this work a convolutional neural networks based algorithm for despeckling is proposed. The network is trained on simulated SAR data. The paper is mainly focused on the implementation of a cost function that takes account of both spatial consistency of image and statistical properties of noise.