Iris Image Acquisition and Real-Time Detection System Using Convolutional Neural Network

The aim is to further improve the efficiency of iris detection and ensure real-time iris data acquisition. Here, the light field refocusing algorithm can collect the data in real-time based on the existing iris data acquisition and detection system, and the DL (Deep Learning) CNN (Convolutional Neural Network) is introduced. Consequently, an iris image acquisition and real-time detection system based on CNN is proposed, and the system for image acquisition, processing, and displaying is constructed based on FPGA (Field Programmable Gate Array). The spatial filtering algorithm can compare the performance of the proposed bilateral filters with common filters. The results indicate that the proposed bilateral filters can pick out qualified iris images in real-time, greatly improving the accuracy of the iris image recognition system. The average time for real-time quality assessment of each frame image is less than 0.05 seconds. The classification accuracy of the iris image quality assessment algorithm based on DL is 96.38%, higher than the other two algorithms, and the average classification error rate is 3.69%, lower than the average error rate of other algorithms. The results can provide a reference for real-time iris image detection and data acquisition.

Single Image De-Hazing via Multiscale Wavelet Decomposition and Estimation with Fractional Gradient-Anisotropic Diffusion Fusion

This paper presents algorithms based on fractional multiscale gradient fusion and multilevel wavelet decomposition for underwater and hazy image enhancement. The algorithms utilize partial differential equation (PDE)-generated low- and high-frequency images fused via gradient domain and anisotropic diffusion. Furthermore, wavelet multi-level decomposition, estimation and adjustment of detail and approximation coefficients are employed in improving local and global enhancement. Solutions to halo effect are also developed using compressive bilateral filters or other nonlinear/nonlocal means filter. Ultimately, experimental comparisons indicate that the proposed methods surpass or are comparable to several algorithms from the literature.

Comparative Analysis of Bilateral Filter and its Variants for Magnetic Resonance Imaging

Background: With the increase in research in the direction of making images noise free a number of algorithms have been designed. Methods: The choice of Denoising method will be made in such a way that it reduces or removes noise content on one hand and on the other hand it preserves the information content of the image. Our article focuses on analyzing the performance of bilateral filters and its derivatives for Denoising of MRI images. Results: The bilateral filter is a hybridized version of basic range filtering and domain filtering techniques. Conclusion: A comparative review of these filters is presented for MRI images taking different altitudes of Gaussian noise added to the image.

Measuring camera Shannon Information Capacity with a Siemens Star Image

Shannon information capacity, which can be expressed as bits per pixel or megabits per image, is an excellent figure of merit for predicting camera performance for a variety of machine vision applications, including medical and automotive imaging systems. Its strength is that is combines the effects of sharpness (MTF) and noise, but it has not been widely adopted because it has been difficult to measure and has never been standardized. We have developed a method for conveniently measuring information capacity from images of the familiar sinusoidal Siemens Star chart. The key is that noise is measured in the presence of the image signal, rather than in a separate location where image processing may be different—a commonplace occurrence with bilateral filters. The method also enables measurement of SNRI, which is a key performance metric for object detection. Information capacity is strongly affected by sensor noise, lens quality, ISO speed (Exposure Index), and the demosaicing algorithm, which affects aliasing. Information capacity of in-camera JPEG images differs from corresponding TIFF images from raw files because of different demosaicing algorithms and nonuniform sharpening and noise reduction.

Denoising of image using bilateral filtering in multiresolution

One of the very efficient and resource conservative image processing methodology is with the help of bilateral filters. This technique filters the image without the help of edge smoothing but it does employs spatial averaging in a non-linear way. The filtering technique discussed above is very much dependent on the parameters of its filters. A very slight change in filter parameter values effects the outputs and results in a most drastic manner. In this paper, the author has worked on two contributions. In the applications concerning image denoising, the author has contributed in study of the parameter selection of bilateral filters which are optimal in nature. The contribution number two is about extending the present work i.e. extension of the filters which are bilateral in nature. In this process, the bilateral filtering of images is applied to the lower frequency sub-bands which is also known as approximation sub-band. This sub-band is obtained by using the wavelet transformations. Hence, a new framework for image denoising will be created which will be combination of multiresolution bilateral filtering and wavelets transformation techniques. As a matter of fact, this combination is efficient in contradicting noise from an image.