Synthetic aperture radar (SAR) images map Earth’s surface at high resolution, regardless of the weather conditions or sunshine phenomena. Therefore, SAR images have applications in various fields. Speckle noise, which has the characteristic of multiplicative noise, degrades the image quality of SAR images, which causes information loss. This study proposes a speckle noise reduction algorithm while using the speckle reducing anisotropic diffusion (SRAD) filter, discrete wavelet transform (DWT), soft threshold, improved guided filter (IGF), and guided filter (GF), with the aim of removing speckle noise. First, the SRAD filter is applied to the SAR images, and a logarithmic transform is used to convert multiplicative noise in the resulting SRAD image into additive noise. A two-level DWT is used to divide the resulting SRAD image into one low-frequency and six high-frequency sub-band images. To remove the additive noise and preserve edge information, horizontal and vertical sub-band images employ the soft threshold; the diagonal sub-band images employ the IGF; while, the low- frequency sub-band image removes additive noise using the GF. The experiments used both standard and real SAR images. The experimental results reveal that the proposed method, in comparison to state-of-the art methods, obtains excellent speckle noise removal, while preserving the edges and maintaining low computational complexity.

Speckle Noise Reduction Technique for SAR Images Using Statistical Characteristics of Speckle Noise and Discrete Wavelet Transform

Deep learning-based compressed image artifacts reduction based on multi-scale image fusion

In this paper, we propose an improved generative adversarial network (GAN) for image compression artifacts reduction task (artifacts reduction by GANs, ARGAN). The lossy compression leads to quite complicated compression artifacts, especially blocking artifacts and ringing effects. To handle this problem, we choose generative adversarial networks as an effective solution to reduce diverse compression artifacts. The structure of “U-NET” style is adopted as the generative network in the GAN. A discriminator network is designed in a convolutional manner to differentiate the restored images from the ground truth distribution. This approach can help improve the performance because the adversarial loss aggressively encourages the output image to be close to the distribution of the ground truth. Our method not only learns an end-to-end mapping from input degraded image to corresponding restored image, but also learns a loss function to train this mapping. Benefit from the improved GANs, we can achieve desired results without hand-engineering the loss functions. The experiments show that our method achieves better performance than the state-of-the-art methods.

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Compression artifacts reduction by improved generative adversarial networks

In this study, techniques were proposed for the detection of epileptic seizures from electroencephalogram (EEG) signals using the wavelet transform (WT), peak extraction and phase–space reconstruction (PSR) based Euclidean distances. In the first step, the wavelet coefficients were extracted after eliminating the noise from the EEG signals using a WT, which is a widely used signal processing technique. In the second step, the peaks were extracted from the wavelet coefficients. In the third step, the continuous peaks that were extracted were mapped to 3D coordinates using PSR. In the fourth step, the Euclidean distances between the mapped 3D coordinates and the origin were obtained. The features of the Euclidean distances obtained were extracted using statistical techniques. The final features extracted were used as inputs to the neural network with weighted fuzzy membership (NEWFM). NEWFM contains the bounded sum of weighted fuzzy memberships (BSWFMs) that can reveal the differences in the graphic characteristics between normal EEG signals and epileptic-seizure EEG signals. The BSWFMs can easily be embedded in a portable device to detect epileptic seizures from EEG signals in real life.

https://www.mdpi.com/2073-8994/12/8/1239/pdf

Detection of Epileptic Seizures Using Wavelet Transform, Peak Extraction and PSR from EEG Signals

Video compression technology is significant to video transmission and storage. However, compression artifacts arise in videos. Specifically, coarse quantization eliminates video details and degrades visual quality. Most of artifact reduction methods use filter processing or Mean Square Error (MSE)loss that leads to over-smoothing results. Moreover, most of the methods target to reduce single image compression artifact instead of video artifact. In this paper, we present an adversarial learning method with recurrent framework called Video Artifact Reduction Generative Adversarial Network (VRGAN). Our network contains a generator with recurrent framework that improves video consistency, a dense block that enhances receptive field for large transform unit, and a relativistic discriminator that evaluates the relationship between the generated frames and the original high-quality frames. Our VRGAN is able to generate more realistic videos. The effectiveness in reducing video compression artifacts of the method has been demonstrated qualitatively and quantitatively. The performance comparison with previous works shows the superiority of the proposed method.

Ming-Te Wu

Papers

Wavelet transform based on Meyer algorithm for image edge and blocking artifact reduction