Ogleyusuf3216

Lossy compression brings artifacts into the compressed image and degrades the visual quality. In recent years, many compression artifacts removal methods based on convolutional neural network (CNN) have been developed with great success. However, these methods usually train a model based on one specific value or a small range of quality factors. Obviously, if the test images quality factor does not match to the assumed value range, then degraded performance will be resulted. With this motivation and further consideration of practical usage, a highly robust compression artifacts removal network is proposed in this paper. Our proposed network is a single model approach that can be trained for handling a wide range of quality factors while consistently delivering superior or comparable image artifacts removal performance. To demonstrate, we focus on the JPEG compression with quality factors, ranging from 1 to 60. Note that a turnkey success of our proposed network lies in the novel utilization of the quantization tables as part of the training data. Furthermore, it has two branches in parallel-i.e., the restoration branch and the global branch. The former effectively removes the local artifacts, such as ringing artifacts removal. On the other hand, the latter extracts the global features of the entire image that provides highly instrumental image quality improvement, especially effective on dealing with the global artifacts, such as blocking, color shifting. read more Extensive experimental results performed on color and grayscale images have clearly demonstrated the effectiveness and efficacy of our proposed single-model approach on the removal of compression artifacts from the decoded image.Tissue harmonic imaging is often the preferred ultrasound imaging modality due to its ability to suppress reverberations. The method requires good control of the transmit stage of the ultrasound scanner, as harmonics in the transmitted ultrasound pulses will interfere with the harmonics generated in the tissue during nonlinear propagation, degrading image quality. In this study, a medical ultrasound probe used in tissue harmonic imaging was experimentally characterized for transmitted second-harmonic distortion to identify and compare the sources of nonlinear distortion in the probe and transmit electronics. The system was tested up to amplitudes above what is found during conventional operation, pushing the system to the limits in order to investigate the phenomenon. Under these conditions, second-harmonic levels up to -20 dB relative to the fundamental frequency were found in the ultrasound pulses transmitted from the probe. The transmit stage consists of high-voltage transmit electronics, cable, tuning inductors, and the acoustic stack. The contribution from the different stages in the ultrasound transmit chain was quantified by separating and measuring at different positions. Nonlinearities in the acoustic transducer stack were identified as the dominating source for second harmonics in the transmitted ultrasound pulses. Contribution from other components, e.g., transmit electronics and cable and tuning circuitry, were found to be negligible compared with that from the acoustic stack. Investigation of the stack's electrical impedance at different driving voltages revealed that the impedance changes significantly as a function of excitation voltage. The second-harmonic peak in the transmitted pulses can be explained by this nonlinear electrical impedance distorting the driving voltage and current.The Wave Controlled Aliasing In Parallel Imaging (Wave-CAIPI) technique manifests great potential to highly accelerate three-dimensional (3D) balanced steady-state free precession (bSSFP) through substantially reducing the geometric factor (g-factor) and aliasing artifacts of image reconstruction. However, severe banding artifacts appear in bSSFP imaging due to unbalanced gradients with nonzero 0th moment applied by the conventional Wave-CAIPI technique. In this study, we propose a 3D Wave-bSSFP scheme that adopts truncated wave gradients with zero 0th moment to avoid introducing additional banding artifacts and to maintain the advantages of wave encoding. The simulation results indicate that the number of wave cycles that are truncated and different options of applying wave gradients affect both the g-factor reduction and image quality, but the influence is limited. In phantom experiments, the proposed technique shows similar acceleration performance as the conventional Wave-CAIPI technique and effectively eliminates its introduced banding artifacts. Additionally, Wave-bSSFP obtains up to 12× retrospective acceleration at 0.8 mm isotropic resolution in in vivo 3D brain experiments and is superior to the state-of-the-art Controlled Aliasing In Parallel Imaging Results IN Higher Acceleration (CAIPIRINHA) technique, according to both visual validation and quantitative analysis. Moreover, in vivo 3D spine and abdomen imaging demonstrate the potential clinical applications of Wave-bSSFP with fast acquisition speed, improved isotropic resolution and fine image quality.With the success of deep learning in classifying short trimmed videos, more attention has been focused on temporally segmenting and classifying activities in long untrimmed videos. State-of-the-art approaches for action segmentation utilize several layers of temporal convolution and temporal pooling. Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors. In this paper, we propose a multi-stage architecture for the temporal action segmentation task that overcomes the limitations of the previous approaches. The first stage generates an initial prediction that is refined by the next ones. In each stage we stack several layers of dilated temporal convolutions covering a large receptive field with few parameters. While this architecture already performs well, lower layers still suffer from a small receptive field. To address this limitation, we propose a dual dilated layer that combines both large and small receptive fields. We further decouple the design of the first stage from the refining stages to address the different requirements of these stages.