The charge-coupled device (CCD) array spectrometers are increasingly being used in wide variety of scientific researches and industrial applications. However, all CCD detectors suffer some amount of non-linear behavior on response to light, and the accuracy of the CCD array spectrometer measurement will be influenced from the non-linear behavior, the detectable error is presented. Therefore, the non-linearity correction method is important to obtain the accurate results of spectrometers based on the CCD. Here, we proposed a convenient experiment and calculation method to solve the problem of non-linearity. With the combined values of all the pixels across the detector, a 7th order polynomial is fitted in the relation between the normalized counts per second and counts, and the correction coefficients were generated by this polynomial for the pixels. The method to apply the correction is dividing the original response by the calculated correction coefficients for all the pixels. Finally, the CCD detector response is linear to >99.5% after correcting for the non-linearity of spectrometers, experimental results show that the proposed method is reasonable and efficient.
In real applications, such as consumer digital imaging, it is very common to record weakly blurred and strongly noisy images. Recently, a state-of-art algorithm named geometric locally adaptive sharpening (GLAS) has been proposed. By capturing local image structure, it can effectively combine denoising and sharpening together. However, there still exist two problems in the practice. On one hand, two hard thresholds have to be constantly adjusted with different images so as not to produce over-sharpening artifacts. On the other hand, the smoothing parameter must be manually set precisely. Otherwise, it will seriously magnify the noise. However, these parameters have to be set in advance and totally empirically. In a practical application, this is difficult to achieve. Thus, it is not easy to use and not smart enough. In an effort to improve the restoration effect of this situation by way of GLAS, an improved GLAS (IGLAS) algorithm by introducing the local phase coherence sharpening Index (LPCSI) metric is proposed in this paper. With the help of LPCSI metric, the two hard thresholds can be fixed at constant values for all images. Compared to the original method, the thresholds in our new algorithm no longer need to change with different images. Based on our proposed IGLAS, its automatic version is also developed in order to compensate for the disadvantages of manual intervention. Simulated and real experimental results show that the proposed algorithm can not only obtain better performances compared with the original method, but it is very easy to apply.
Image blind deconvolution is a more practical inverse problem in modern imaging sciences including consumer photography, astronomical imaging, medical imaging, and microscopy imaging. Among all of the latest blind deconvolution algorithms, the total variation based method provides privilege for large blur kernel. However, the computation cost is heavy and it does not handle the estimated kernel error properly. Otherwise, the using of the whole image to estimate the blur kernel is inaccurate because of that the insufficient edges information will hazard the accuracy of estimation. Here, we proposed a robust multi-frame images blind deconvolution algorithm to handle this complicated imaging model and applying it to the engineering community. In our proposed method, we induced the patch and kernel selection scheme to selecting the effective patch to estimate the kernel without using the whole image; then an total variation based kernel estimation algorithm was proposed to estimate the kernel; after the estimation of blur kernels, a new kernel refinement scheme was applied to refine the pre-estimated multi-frame estimated kernels; finally, a robust non-blind deconvolution method was implemented to recover the final latent sharp image with the refined blur kernel. Objective experiments on both synthesized and real images evaluate the efficiency and robustness of our algorithm and illustrate that this approach not only have rapid convergence but also can effectively recover high quality latent image from multi-blurry images.
Because of the substrate back reflectance phenomena, the reflectance of optical thin film stack on a transparent substrate is totally different from that of on an opaque substrate. In this paper, a method for the measurement of low reflectance optical film thickness that has substrate back reflectance is proposed for the first time. Through the analysis of the actual substrate back reflectance, a compensation model is introduced to reduce the influence of substrate back reflectance. The experimental results show good fitting precision and proves that this model can be used directly for the measurement of the optical film thickness with substrate back reflectance, and no extra process is needed.
As a kind of film device, band-pass filter is widely used in pattern recognition, infrared detection, optical fiber communication, etc. In this paper, an algorithm for automatic measurement of band-pass filter quality criterion is proposed based on the proven theory calculation of derivate spectral transmittance of filter formula. Firstly, wavelet transform to reduce spectrum data noises is used. Secondly, combining with the Gaussian curve fitting and least squares method, the algorithm fits spectrum curve and searches the peak. Finally, some parameters for judging band-pass filter quality are figure out. Based on the algorithm, a pipeline for band-pass filters automatic measurement system has been designed that can scan the filter array automatically and display spectral transmittance of each filter. At the same time, the system compares the measuring result with the user defined standards to determine if the filter is qualified or not. The qualified product will be market with green color, and the unqualified product will be marked with red color. With the experiments verification, the automatic measurement system basically realized comprehensive, accurate and rapid measurement of band-pass filter quality and achieved the expected results.
Cooled CCD (charge coupled device) imaging camera has found wide application in the field of astronomy,
color photometry, spectroscopy, medical imaging, densitometry, chemiluminescence and epifluorescence imaging. A
Cooled CCD (CCCD) imaging camera differs from traditional CCD/CMOS imaging camera in that Cooled CCD
imaging camera can get high resolution image even in the low illumination environment. SNR (signal noise ratio) is most
popular parameter of digital image quality evaluation. Many researchers have proposed various SNR testing methods for
traditional CCD imaging camera, however, which is seldom suitable to Cooled CCD imaging camera because of
different main noise source. In this paper, a new testing method of SNR is proposed to evaluate the quality of image
captured by Cooled CCD. Stationary Wavelet Transform (SWT) is introduced in the testing method for getting more
exact image SNR value. The method proposed take full advantage of SWT in the image processing, which makes the
experiment results accuracy and reliable. To further refining SNR testing results, the relation between SNR and
integration time is also analyzed in this article. The experimental results indicate that the testing method proposed
accords with the SNR model of CCCD. In addition, the testing values for one system are about one value, which show
that the proposed testing method is robust.