Speckle, a multiplicative granular noise, inherently appears in coherent imaging techniques such as synthetic aperture radar (SAR). It deteriorates the visual quality of images, which leads to difficulty in image interpretation for further analysis. Hence, speckle filtering is essential to recover the image details for applications like segmentation and classification. Several despeckling techniques have been developed in the literature, among which anisotropic diffusion (AD) and discrete wavelet transform (DWT) based methods have achieved state-of-the-art despeckling performance. However, AD cannot be employed indefinitely owing to blurring and detail loss. Similarly, DWT produces spurious noise around edges. This paper proposes a high-performance despeckling technique that uses modified speckle reducing anisotropic diffusion as the preprocessing step in a homomorphic architecture. The architecture uses discrete wavelet transform, dynamic weighted adaptive thresholding (DWAT), weighted least squares, and guided filtering to recover the clean image. In addition, to enhance the performance of the despeckling process, a convolutional neural network (CNN) is used as a subsequent processing module to remove residual speckle while preserving the edges. The CNN uses a supervised learning paradigm trained on simulated speckled and clean image pairs to fine-tune the despeckled output. Subjective (visual) and objective evaluations on both simulated and real SAR datasets demonstrate that the proposed hybrid approach achieves robust despeckling performance, particularly excelling in edge preservation, radiometric consistency, and detail reconstruction across varied scene types as compared to the existing methods.

Speckle is a multiplicative granular noise that naturally occurs in the images captured by coherent imaging sensors such as synthetic aperture radar (SAR). It visually degrades the underlying image information and has an impact on subsequent image analysis. This problem is addressed here by developing a sparse representation model and applying an alternating minimization scheme for SAR image despeckling. The proposed method directly deals with the multiplicative noise and the data model is formed by utilizing the speckle statistics. The similar patches are clustered together to adaptively learn the dictionary, and the sparse coefficients are updated using plug-and-play based fast iterative shrinkage threshold algorithm (PnP-FISTA). Finally, the clean image is estimated using Newton’s method. Experiments on simulated and practical SAR images signify that the proposed method performs better compared to the state-of-the-art methods in terms of performance metrics and visual assessment.

Images acquired by any imaging system suffers from arbitrary variation in the intensity values, abrupt changes in the illumination and low contrast. Such images lack utilizable information and suffers from visual interpretability. Subsidiary information from such images are extracted by removing the noise, sharpening contrast and detection of the edges by using several filtering techniques. Median filtering is one such non linear based method which removes range isolated noise like salt and pepper noise while preserving the edge information. But median filtering fails when the image is corrupted by extremely high impulse noise. Switching based median filtering is able to perform the noise removal in the case of high intensity impulse noise. This brief introduces an efficient parallel architecture for implementation of switching based median filtering technique using Field Programmable Gate Array (FPGA) prototyping. The results are analysed based on the hardware requirement, power consumption, and speed of the architecture. The FPGA result is validated by implementing the above algorithm in MATLAB when the images are corrupted by noise models.