Restoration of motion-blurred image based on border deformation detection: a traffic sign restoration model

Improved estimation of motion blur parameters for restoration from a single image

This paper presents an improved method to estimate the blur parameters of motion deblurring algorithm for single image restoration based on the point spread function (PSF) in frequency spectrum. We then introduce a modification to the Radon transform in the blur angle estimation scheme with our proposed difference value vs angle curve. Subsequently, the auto-correlation matrix is employed to estimate the blur angle by measuring the distance between the conjugated-correlated troughs. Finally, we evaluate the accuracy, robustness and time efficiency of our proposed method with the existing algorithms on the public benchmarks and the natural real motion blurred images. The experimental results demonstrate that the proposed PSF estimation scheme not only could obtain a higher accuracy for the blur angle and blur length, but also demonstrate stronger robustness and higher time efficiency under different circumstances.

PSF model for diffractive optical elements with improved imaging performance in dual-waveband infrared systems

Single-layer diffractive optical elements (SLDOEs) have advantages in terms of configuration, fabrication, range of angles and cost; however, the diffraction efficiency decreases sharply with wavelength deviating from the design wavelength, especially for dual-waveband imaging, causing apparent image blur. We propose a point spread function (PSF) model affected by the diffraction efficiency, which is called PSF_DOE, and a method of restoring the blurred image to improve imaging performance in dual-waveband infrared systems with an SLDOE. Then, a design example of cooled MWIR and LWIR is presented. Furthermore, imaging simulations with different grades noises and restorations are conducted. Results reveal that the PSF_DOE model can significantly improve the image blur caused by the decreased diffraction efficiency.

Recognition of Traffic Sign Based on Bag-of-Words and Artificial Neural Network

The traffic sign recognition system is a support system that can be useful to give notification and warning to drivers. It may be effective for traffic conditions on the current road traffic system. A robust artificial intelligence based traffic sign recognition system can support the driver and significantly reduce driving risk and injury. It performs by recognizing and interpreting various traffic sign using vision-based information. This study aims to recognize the well-maintained, un-maintained, standard, and non-standard traffic signs using the Bag-of-Words and the Artificial Neural Network techniques. This research work employs a Bag-of-Words model on the Speeded Up Robust Features descriptors of the road traffic signs. A robust classifier Artificial Neural Network has been employed to recognize the traffic sign in its respective class. The proposed system has been trained and tested to determine the suitable neural network architecture. The experimental results showed high accuracy of classification of traffic signs including complex background images. The proposed traffic sign detection and recognition system obtained 99.00% classification accuracy with a 1.00% false positive rate. For real-time implementation and deployment, this marginal false positive rate may increase reliability and stability of the proposed system.

Real-time traffic sign recognition based on a general purpose GPU and deep-learning

We present a General Purpose Graphics Processing Unit (GPGPU) based real-time traffic sign detection and recognition method that is robust against illumination changes. There have been many approaches to traffic sign recognition in various research fields; however, previous approaches faced several limitations when under low illumination or wide variance of light conditions. To overcome these drawbacks and improve processing speeds, we propose a method that 1) is robust against illumination changes, 2) uses GPGPU-based real-time traffic sign detection, and 3) performs region detecting and recognition using a hierarchical model. This method produces stable results in low illumination environments. Both detection and hierarchical recognition are performed in real-time, and the proposed method achieves 0.97 F1-score on our collective dataset, which uses the Vienna convention traffic rules (Germany and South Korea).