# An Advanced SAR Image Despeckling Method by Bernoulli-Sampling-Based Self-Supervised Deep Learning
## 3 Experimental Results and Analysis
### Despeckling Experiments on Synthetic Speckled Data
Compared to SAR-RDCP, SAR-DRN had larger PSNR values for _Beach_, _Parking_, and _School_. On the contrary, the SSIM values of SAR-RDCP (i.e., _Airport_, _Parking_, and _School_)  
were larger than those of SAR-DRN. Generally speaking, it is difficult to maintain noise smoothing (i.e., PSNR and ENL) and feature preservation (i.e., SSIM) at the same time. However, our proposed SSD-SAR-BS gained about a 0.07-0.62 advancement in terms of the PSNR, compared to SAR-DRN. At the same time, our proposed SSD-SAR-BS gained about a 0.02-0.04 advancement in terms of the SSIM, compared to SAR-RDCP. Besides, combining its larger ENL values, in summary, our proposed SSD-SAR-BS achieved the best quantitative results in terms of the PSNR, SSIM, and ENL. This means that the results of our proposed SSD-SAR-BS were closer to the original speckle-free target images, with better structural feature preservation and better speckle noise suppression. Except the quantitative evaluation, visual assessment is also necessary for comprehensive analysis of despeckling performance. We present the one-look speckled input images, the despeckling results obtained by the compared methods and our proposed SSD-SAR-BS, and the original speckle-free target images in Figures 6-9. To give detailed contrasting results, we also provide the corresponding magnified results in Figure 10. The PPB removed most speckle noise, while its results showed significant oversmoothing. In other words, the detailed texture features in the results of PPB were also lost along with the speckle noise. SAR-BM3D showed an acceptable tradeoff between speckle noise suppression and detailed feature preservation. However, SAR-BM3D showed the blocking phenomenon. The blocking artefacts made its results look mottled and unnatural, which can be easily found in Figure 7c. As shown in quantitative evaluation, the deep-learning-based methods obtained clearer results compared to the traditional ones, especially for SAR-DRN and SAR-RDCP. However, when compared to our proposed SSD-SAR-BS, their performance was still not good enough. This can be explained according to two aspects: (1) Edge preservation as marked in the red circles of Figures 10(1,3,4): The linear edge features were lost or intermittent in the magnified results of SAR-DRN and SAR-RDCP. Meanwhile, they still could be found or kept intact in the results of our proposed SSD-SAR-BS. (2) A dense small point area in the magnified results of _Beach_ (i.e., Figure 10(2)): dense small points were incorrectly transformed to lines or blocks in the magnified results of SAR-DRN and SAR-RDCP. This may be due to the visual similarity between dense small points and speckle noise. SAR-DRN and SAR-RDCP cannot accurately separate dense point targets and speckle noise. To remove speckle noise, dense small points were also innocently removed in their despeckling results. Although it was still not perfect compared to the speckle-free target (i.e., Figure 10(2-h)), the dense small points were retained with their original visual form in the result of our proposed SSD-SAR-BS (i.e., Figure 10(2-g)). In other words, they were points rather than lines or blocks.