Combined with the design and construction of transmission lighting system, the machine vision detection scheme is proposed for bottle cap gap width inspection of industrial production line. With the consideration of the reflective properties of glass bottle material, the transmission lighting method using white LED array light source is designed.. It could avoid the disturbance on the detection algorithm caused by the nonuniformity of the imaging region. The vision detection algorithm is developed by using automatic bottle cap region search method and edge detection by seed growing. The upper and bottom edge line position is extracted precisely and the real bottle cap gap width is calculated by image pixel physical size calibration. The real experimental imaging system is setup and the experimental results demonstrate that the presented machine vision detection scheme could realize high precision bottle cap sealing gap width detection with detection precision as 0.01mm. The vision algorithm has high accuracy and the processing speed could satisfy the production line task requirements for bottle cap detection.
Based on multiple fields of view (FOV) point spread function (PSF) estimation, we propose a novel gradient-constrained image restoration method to solve optical degradation in microscopic imaging. The whole FOV is segmented into several parts. The modulation transfer function (MTF) is measured to obtain the corresponding PSF for each part. L0 gradient constraint is treated as a regularization term, a fast image restoration method is designed to deblur degraded images of each field of view. Finally, gradual weight approach is used to stitch the multiple field of view (m-FOV) restoration images. Several microscopic images are tested and evaluated. Comparing with other methods, the results indicate that our method performs better, and runs fastest of all.
In this paper, a motion deblurring method with long/short exposure image pairs is presented. The long/short exposure image pairs are captured for the same scene under different exposure time. The image pairs are treated as the input of the deblurring method and more information could be used to obtain a deblurring result with high image quality. Firstly, the luminance equalization process is carried out to the short exposure image. And the blur kernel is estimated with the image pair under the maximum a posteriori (MAP) framework using conjugate gradient algorithm. Then a L0 image smoothing based denoising method is applied to the luminance equalized image. And the final deblurring result is obtained with the gain controlled residual image deconvolution process with the edge map as the gain map. Furthermore, a real experimental optical system is built to capture the image pair in order to demonstrate the effectiveness of the proposed deblurring framework. The long/short image pairs are obtained under different exposure time and camera gain control. Experimental results show that the proposed method could provide a superior deblurring result in both subjective and objective assessment compared with other deblurring approaches.
In the field of deep space science detection and high resolution earth observation, a relatively high motion velocity is often generated between the optical camera and the imaging target. Images obtained during the exposure time can produce image motion blur, which becomes one of the main obstacles to acquire high resolution image near the target. As an extended task of the third phase of China’s lunar exploration program, flight imaging of the planned sampling area of Chang’e-5 was carried out. A dual resolution camera with a wide field of view (FOV) camera and a narrow FOV camera was used for imaging mission. High flying speed causes the generation of large motion blurred images captured by the narrow FOV camera and the motion blur can be up to around 30 pixels. To deal with this problem, we analyzed the image features of the blurred images captured by the narrow FOV camera, and proposed a corresponding method that can estimate image motion value from the blurred lunar image based on small craters detection scheme and then adopted the regularization method to restore the image. The algorithm is applied in the batch processing of the real blurred lunar images and has achieved a significant restored effect.
Fast image restoration method is proposed for vibration image deblurring based on coded exposure and vibration detection. The criterion of the code sequence selection is discussed in detail, and several factors are considered to search for the optimal coded exposure sequence. The blurred vibration image is obtained by the coded exposure technique. Meanwhile, the vibration track information of the camera is detected by a fiber-optic gyroscope. The point spread function (PSF) is estimated using a statistical method with the selected code sequence and vibration track information. Finally, the blurred image is quickly restored with the estimated PSF through a direct inverse filtering method. Simulation experiments are conducted to test the performance of the approach with different vibration forms. A real imaging system is constructed to verify the effectiveness of the proposed algorithm. Experimental results show that the presented algorithm could yield better subjective experiences and superior objective evaluation values.
How to remove the noise in infrared image effectively with detail preserving is a significant but difficult problem in infrared image processing. Various methods have been proposed to obtain good results. However, these algorithms usually cannot distinguish noise and detail efficiently, which leads to smoothing some details in infrared images. Recently a novel local measure called relative total variation (RTV) is proposed to accomplish effective texture removal. RTV measure is combined with a general windowed total variation measure and a novel inherent variation measure to smooth the image texture effectively while preserving the main structure. In this paper, using detail preserving smoothing method via RTV, a multi scale denoising algorithm for infrared image is proposed. Firstly, the infrared image is decomposed into several scales by non-subsampled Contourlet transform (NSCT). NSCT decomposition does not do any down sampling or up sampling, thus the results are not band limited. Secondly，the algorithm applies RTV based detail preserving denoising method for each decomposed layers. Different smoothing parameters are respectively used to adjust the denoising levels in different scales. Finally, various synthetic weights are utilized to different layers to reconstruct the final infrared denosing results. Compared with other infrared denoising approaches, the quantitative comparisons demonstrate that the proposed method could well suppress the noise of infrared image while preserving the edge details effectively. Both visual quality and objective measure results show that this method is efficient and has a good application in infrared image denoising.