The fusion of multispectral (MS) and panchromatic (PAN) images is a useful technique for enhancing the spatial quality of low-resolution MS images. Liu recently proposed the smoothing-filter-based intensity modulation (SFIM) fusion technique. This technique upscales MS images using bicubic interpolation and introduces high-frequency information of the PAN image into the MS images. However, this fusion technique is plagued by blurred edges if the upscaled MS images are not accurately coregistered with the PAN image. In the first part of this letter, we propose the use of the Induction scaling technique instead of bicubic interpolation to obtain sharper, better correlated, and hence better coregistered upscaled images. In the second part, we propose a new fusion technique derived from induction, which is named ldquoIndusion.rdquo In this method, the high-frequency content of the PAN image is extracted using a pair of upscaling and downscaling filters. It is then added to an upscaled MS image. Finally, a comparison of SFIM (with both bicubic interpolation and induction scaling) is presented along with the fusion results obtained by IHS, discrete wavelet transform, and the proposed Indusion techniques using Quickbird satellite images.

High-dynamic-range (HDR) images require tone mapping to be displayed properly on lower dynamic range devices. In this paper, a tone-mapping algorithm that uses histogram of luminance to construct a lookup table (LUT) for tone mapping is presented. Characteristics of the human visual system (HVS) are used to give more importance to visually distinguishable intensities while constructing the histogram bins. The method begins with constructing a histogram of the luminance channel, using bins that are perceived to be uniformly spaced by the HVS. Next, a refinement step is used, which removes the pixels from the bins that are indistinguishable by the HVS. Finally, the available display levels are distributed among the bins proportionate to the pixels counts thus giving due consideration to the visual contribution of each bin in the image. Quality assessment using both quantitative evaluations and user studies suggests that the presented algorithm produces tone-mapped images that are visually pleasant and preserve details of the original image better than the existing methods. Finally, implementation details of the algorithm on GPU for parallel processing are presented, which could achieve a significant gain in speed over CPU-based implementation.

International audienceThis article presents a novel method for the enhancement of the spatial quality of hyperspectral (HS) images through the use of a high resolution panchromatic (PAN) image. Due to the high number of bands, the application of a pan-sharpening technique to HS images may result in an increase of the computational load and complexity. Thus a dimensionality reduction preprocess, compressing the original number of measurements into a lower dimensional space, becomes mandatory. To solve this problem, we propose a pan-sharpening technique combining both dimensionality reduction and fusion, making use of non-linear principal component analysis (NLPCA) and Indusion, respectively, to enhance the spatial resolution of a HS image. We have tested the proposed algorithm on HS images obtained from CHRIS-Proba sensor and PAN image obtained from World view 2 and demonstrated that a reduction using NLPCA does not result in any significant degradation in the pan-sharpening results

Advances in both parallel processing capabilities because of graphical processing units (GPUs) and computer vision algorithms have led to the development of deep neural networks (DNN) and their utilization in real-world applications. Starting from the LeNet-5 architecture of the 1990s, modern deep neural networks may have tens to hundreds of layers to solve complex problems such as license plate detection or recognition tasks. In this manuscript, we present a review of the state-of-the-art methods related to automatic license plate recognition. Since deep networks have demonstrated a remarkable ability to outperform other machine learning techniques, we focus only on neural network based license plate recognition methods. We highlight the particular types of networks, i.e., convolutional, residual recurrent, or long-short-term-memory, used for the specific tasks of license plate detection, extraction, or recognition in different existing works. The presented summary also highlights some of the most widely used data sets for comparison and shares the results reported in the reviewed papers. We also give an overview of the effects of fog, motion, or the use of synthetic data on license plate recognition. Finally, promising directions for future research in this domain are presented.

We propose a novel class of schemes for the pansharpening of multispectral (MS) images using a multivariate empirical mode decomposition (MEMD) algorithm. MEMD is an extension of the empirical mode decomposition (EMD) algorithm, which enables the decomposition of multivariate data into its intrinsic oscillatory scales. The ability of MEMD to process multichannel data directly by performing data-driven, local, and multiscale analysis makes it a perfect match for pansharpening applications, a task for which standard univariate EMD is ill-equipped due to the nonuniqueness, mode-mixing, and mode-misalignment issues. We show that MEMD overcomes the limitations of standard EMD and yields improved spatial and spectral performance in the context of pansharpening of MS images. The potential of the proposed schemes is further demonstrated through comparative analysis against a number of standard pansharpening algorithms on both simulated Pleiades and real-world IKONOS data sets.

Automatic License Plate Recognition (ALPR) has remained an active research topic for years due to various applications, especially in Intelligent Transportation Systems (ITS). This paper presents an efficient ALPR technique based on deep learning, which accurately performs license plate (LP) recognition tasks in an unconstrained environment, even when trained on a limited dataset. We capture real traffic videos in the city and label the LPs and the alphanumeric characters in the LPs within different frames to generate training and testing datasets. Data augmentation techniques are applied to increase the number of training and testing samples. We apply the transfer learning approach to train the recently released YOLOv5 object detecting framework to detect the LPs and the alphanumerics. Next, we train a convolutional neural network (CNN) to recognize the detected alphanumerics. The proposed technique achieved a recognition rate of 92.8% on a challenging proprietary dataset collected in several jurisdictions of Saudi Arabia. This accuracy is higher than what was achieved on the same dataset by commercially available Sighthound (86%), PlateRecognizer (67%), OpenALPR (77%), and a state-of-the-art recent CNN model (82%). The proposed system also outperformed the existing ALPR solutions on several benchmark datasets.