A New Method for Human Microcirculation Image Enhancement

Microcirculation images often have uneven illumination and low contrast in the acquisition process, which affect the image reorganization and following process. This paper presents a new method for microcireulatory image illumination correction and contrast enhancement based on the Contourlet transform. Initially, the image illumination model is extracted by Contourlet transform and then uneven illumination is corrected. Next, in order to restrain noise and enhance image contrast, the probability function associated with noise coefficient and edge coefficient is established and applied to all Contourlet coefficients. Then, a nonlinear enhancement function is applied to modified Contourlet coefficient to adaptively enhance image contrast. Finally, the enhanced image is obtained by inverse Contourlet transform. We compare this approach with other contrast enhancement methods, result showing that our method has a better effect than other enhancement methods, which might be helpful for clinical diagnostics of microcirculation.

Automatic detection of pecan fruits based on Faster RCNN with FPN in orchard

Although the development of the robot picking vision system is widely applied,it is very challenging for fruit detection in orchards with complex light and environment,especially for fruit colors similar to the background.In recent,there are few studies on pecan fruit detection and location based on machine vision.In this study,an accurate and efficient pecan fruit detection method was proposed based on machine vision under natural pecan orchards.In order to solve the illumination problem,a light compensation algorithm was first utilized to process the collected samples,and then an improved Faster Region Convolutional Neural Network(Faster RCNN)with the Feature Pyramid Networks(FPN)was established to train the samples.Finally,the pecan number counting method was introduced to count the number of pecan.A total of 241 pecan images were tested,and comparison experiments were carried out.The mean average precision(mAP)of the proposed detection method was 95.932%,compared with the result without uneven illumination correction(UIC),which was increased by 0.849%,while the mAP of the Single Shot Detector(SSD)+FPN was 92.991%.In addition,the number of clusters was counted using the proposed method with an accuracy rate of 93.539%compared with the actual clusters.The results demonstrate that the proposed network has good robustness for pecan fruit detection in different illumination and various unstructured environments,and the experimental achievement has great potential for robot-picking visual systems.

1.2 Gbit/s visible light transmission based on orthogonal frequency-division multiplexing using a phosphorescent white light-emitting diode and a pre-equalization circuit

We propose a two-cascaded, constant-resistance, symmetrical bridged-T amplitude equalizer for a high-speed visible light communication（VLC） system. With the pre-equalization circuit, the-3 d B bandwidth of the VLC system can be extended from 12 to 235 MHz using a commercially available phosphorescent white light-emitting diode（LED）, a blue filter, and a low-cost PIN photodiode. The data rate is 1.20 Gbit/s, exploiting 16-quadrature amplitude modulation-orthogonal frequency-division multiplexing with a 300 MHz modulation bandwidth over50 cm of free-space transmission under the pre-forward error correction limit of 3.8 × 10^-3. To our knowledge,this is the highest-3 d B bandwidth and the highest data rate ever achieved by using a pre-equalization circuit and white LED in a VLC system.