Source-linked AI summary
SAR Image Despeckling Using a Convolutional Neural Network
Puyang Wang, He Zhang, Vishal M. Patel
TL;DR
Speckle makes SAR image processing and interpretation difficult, motivating an automated despeckling method. ID-CNN directly estimates speckle with a CNN and division-residual layer, trained using Euclidean and TV losses. Experiments on synthetic and real SAR images report improved performance over state-of-the-art despeckling methods.
Problem
Multiplicative speckle contaminates SAR images and makes their processing and interpretation difficult.
Method
ID-CNN uses convolutional layers with batch normalization and ReLU activations, estimates speckle through a division-residual layer, and is trained with Euclidean and TV losses.
Results
ID-CNN achieves the best performance among compared methods at all three synthetic-image noise levels and outperforms competing methods in all four real-image homogeneous blocks.
Takeaways & Limitations
The method generates a despeckled SAR image through a single feedforward process and is also valuable for SAR image understanding tasks such as road detection.
Abstract
from arXiv · showhide
Synthetic Aperture Radar (SAR) images are often contaminated by a multiplicative noise known as speckle. Speckle makes the processing and interpretation of SAR images difficult. We propose a deep learning-based approach called, Image Despeckling Convolutional Neural Network (ID-CNN), for automatically removing speckle from the input noisy images. In particular, ID-CNN uses a set of convolutional layers along with batch normalization and rectified linear unit (ReLU) activation function and a component-wise division residual layer to estimate speckle and it is trained in an end-to-end fashion using a combination of Euclidean loss and Total Variation (TV) loss. Extensive experiments on synthetic and real SAR images show that the proposed method achieves significant improvements over the state-of-the-art speckle reduction methods.
I. INTRODUCTION
SAR provides high-resolution imaging in conditions that limit optical and infrared systems, but multiplicative speckle complicates image processing and interpretation. The paper proposes ID-CNN, which directly estimates speckle from noisy SAR images using a CNN-based architecture.
- SAR produces high-resolution terrain and target images and operates at night and in adverse weather conditions.
- Multiplicative speckle arises from constructive and destructive interference within resolution cells and makes SAR interpretation difficult.
- Existing despeckling methods include multi-look processing, filters, wavelet methods, BM3D, and Total Variation methods.
- Deep-learning despeckling has been relatively little studied, motivating the proposed CNN-based ID-CNN approach.
- ID-CNN directly estimates speckle from input images using convolutional layers, batch normalization, ReLU activations, and a component-wise division-residual layer.
II. PROPOSED METHOD
ID-CNN learns a mapping from noisy SAR images to despeckled images without logarithmic preprocessing. Its division-residual design estimates speckle and divides the input by that estimate to obtain the output.
- ID-CNN learns a direct mapping from an input SAR image to a despeckled image using a CNN architecture.
- The network avoids logarithmic transformation by estimating speckle directly from the input image.
- The output before the division-residual layer represents estimated speckle, and dividing the noisy input by it produces the despeckled image.
A. Network Architecture
The ID-CNN noise-estimating component uses eight convolutional layers with batch normalization and ReLU activations, followed by division-residual processing and a final hyperbolic tangent layer.
- Network Architecture: The noise-estimating network contains eight convolutional layers with batch normalization and ReLU activations.
- Network Architecture: Zero-padding preserves the input image dimensions through the convolutional layers.
- Network Architecture: Each non-final convolutional layer uses 64 filters with stride one before the division-residual layer divides the input by estimated speckle.
- Network Architecture: A hyperbolic tangent layer is placed at the network end as a nonlinear function.
B. Loss Function
The loss function combines per-pixel Euclidean loss with Total Variation loss to balance pixel-level detail and image smoothness during ID-CNN training.
- B. Loss Function: Euclidean loss measures per-pixel differences between the despeckled output and corresponding ground truth image.The image pair consists of noisy input Y and ground truth X.
- B. Loss Function: Total Variation loss is added because Euclidean loss can produce artifacts in reconstructed images.The additional term encourages smoother results.
- B. Loss Function: The overall objective is L = LE + λTV LTV, combining Euclidean and Total Variation losses.λTV > 0 controls the importance assigned to the TV term.
- B. Loss Function: The combined loss is intended to preserve pixel-level details while maintaining smoothness in the recovered image.The image dimensions are assumed to be W × H.
III. EXPERIMENTAL RESULTS
The experiments evaluate ID-CNN against six despeckling algorithms on synthetic and real SAR images, using comparison settings drawn from the respective methods’ papers.
- III. EXPERIMENTAL RESULTS: ID-CNN is compared with Lee, Kuan, PPB, SAR-BM3D, CNN, and SAR-CNN despeckling algorithms.The authors identify PPB, SAR-BM3D, CNN, and SAR-CNN as recent state-of-the-art restoration algorithms.
- III. EXPERIMENTAL RESULTS: Experiments cover both synthetic and real SAR images.Comparison-method parameters are set as recommended in their corresponding papers.
- III. EXPERIMENTAL RESULTS: The basic CNN baseline uses the network structure proposed in its cited work and is trained using the same training approach.This establishes a matched baseline for the experimental comparison.
A. Ablation Study
The ablation study examines how progressively adding loss components affects ID-CNN despeckling results, beginning with Euclidean loss alone.
- A. Ablation Study: The study adds the proposed losses one by one and compares the resulting network configurations.This isolates the effects of different loss functions.
- A. Ablation Study: With only Euclidean loss and L = 1, most speckle is removed but artifacts remain in the denoised image.The restored image from this configuration is shown in Figure 5(a).
B. Results on Synthetic Images
On synthetic speckled images across three noise levels, ID-CNN achieves the best reported performance among the compared despeckling methods, while the division-residual design and combined loss are identified as significant.
- B. Results on Synthetic Images: ID-CNN provides the best PSNR, SSIM, and UQI performance at all three tested noise levels.The number of looks is L = 1, 4, and 10.
- B. Results on Synthetic Images: The directly learned noisy-to-clean CNN performs worse than ID-CNN and PPB in many cases.The baseline CNN is trained with Euclidean loss.
- B. Results on Synthetic Images: The results demonstrate the significance of the component-wise division residual layer and Euclidean + TV loss for image despeckling.This conclusion is drawn from the comparative synthetic-image experiment.
C. Results on Real SAR Images
On real SAR images, ID-CNN is evaluated using ENL in homogeneous regions and visual inspection because clean reference images are unavailable. It outperforms the compared methods across homogeneous blocks, while filter-based methods produce blurrier edges and some alternatives show artifacts.
- Quantitative evaluation: ID-CNN achieves higher ENL than the compared despeckling methods in all four homogeneous real-SAR image blocks.ENL is estimated from homogeneous regions marked with red boxes in Figure 3.
- Visual evaluation: Figure 3 orders the columns as SAR input, PPB, SAR-BM3D, SAR-CNN, and ID-CNN from left to right.
- Visual evaluation: ID-CNN shows no obvious speckle, whereas PPB and SAR-CNN retain noticeable artifacts in the real-image comparisons.
- Visual evaluation: PPB and SAR-BM3D generally produce blurrier edges than SAR-CNN and ID-CNN.
D. Runtime Comparisons
After training, ID-CNN has the lowest reported runtime complexity for despeckling a 256 × 256 image. Its lower runtime is possibly related to using eight fully convolutional layers rather than seventeen in other CNN-based methods.
- Runtime comparisons: ID-CNN exhibits the lowest runtime complexity among the compared methods after training.The runtime comparison concerns despeckling an image of size 256 × 256.
- Runtime comparisons: ID-CNN uses eight fully convolutional layers, compared with seventeen in other CNN-based methods.The paper identifies this architectural difference as a possible reason for ID-CNN's lower runtime.
IV. CONCLUSION
The paper concludes that ID-CNN performs speckle reduction through a single feedforward CNN process that estimates noise and divides the noisy input by it. Results on synthetic and real SAR data are promising, with potential value for several SAR image-understanding tasks.
- Conclusion: ID-CNN generates a despeckled SAR image through a single feedforward process using CNN-based noise estimation and division of the noisy input by estimated noise.
- Conclusion: Synthetic and real SAR experiments produce promising qualitative and quantitative results for the proposed method.
- Conclusion: The process is described as valuable for road, railway, ship-wake, agricultural texture, and coastline detection tasks.