High-quality image restoration in real time is a challenge for infrared imaging systems. We present a fast approach to infrared image restoration based on shrinkage functions calibration. Rather than directly modeling the prior of sharp images to obtain the shrinkage functions, we calibrate them for restoration directly by using the acquirable sharp and blurred image pairs from the same infrared imaging system. The calibration method is employed to minimize the sum of squared errors between sharp images and restored images from the blurred images. Our restoration algorithm is noniterative and its shrinkage functions are stored in the look-up tables, so an architecture solution of pipeline structure can work in real time. We demonstrate the effectiveness of our approach by testing its quantitative performance from simulation experiments and its qualitative performance from a developed wavefront coding infrared imaging system.
KEYWORDS: 3D displays, Signal to noise ratio, Detection and tracking algorithms, Target detection, Infrared detectors, Infrared radiation, Infrared imaging, Image processing, 3D acquisition, Optical engineering
Dim targets are extremely difficult to detect using methods based on single-frame detection. Radiation accumulation is one of the effective methods to improve signal-to-noise ratio (SNR). A detection approach based on radiation accumulation is proposed. First, a location space and a motion space are established. Radiation accumulation operation, controlled by vectors from the motion space, is applied to the original image space. Then, a new image space is acquired where some images have an improved SNR. Second, quasitargets in the new image space are obtained by constant false-alarm ratio judging, and location vectors and motion vectors of quasitargets are also acquired simultaneously. Third, the location vectors and motion vectors are mapped into the two spaces, respectively. Volume density function is defined in the motion space. Location extremum of the location space and volume density extremum of motion space will confirm the true target. Finally, actual location of the true target in the original image space is obtained by space inversion. The approach is also applicable to detect multiple dim targets. Experimental results show the effectiveness of the proposed approach and demonstrate the approach is superior to compared approaches on detection probability and false alarm probability.
Artefacts and noise degrade the decoded image of a wavefront coding infrared imaging system, which usually results in the decoded image being inferior to the in-focus infrared image of a conventional infrared imaging system. The previous letter showed that the decoded image fell behind the in-focus infrared image. For comparison, a bar target experiment at temperature of ﹢20°C and two groups of outdoor experiments at temperatures of ﹢28°C and ﹢70°C are respectively conducted. Experimental results prove that a wavefront coding infrared imaging system can achieve the decoded image being approximating to its corresponding in-focus infrared image.
It's well-know that the focal shift of infrared lens is the major factor in degeneration of imaging quality when temperature change. In order to figure out the connection between temperature change and focal shift, partial differential equations of thermal effect on light path are obtained by raytrace method, to begin with. The approximately solution of the PDEs show that focal shift is proportional to temperature change. And a formula to compute the proportional factor is given. In order to understand infrared lens thermal effect deeply, we use defocus by image plane shift at constant temperature to equivalently represent thermal effect on infrared lens. So equivalent focal shift (EFS) is defined and its calculating model is proposed at last. In order to verify EFS and its calculating model, Physical experimental platform including a motorized linear stage with built-in controller, blackbody, target, collimator, IR detector, computer and other devices is developed. The experimental results indicate that EFS make the image plane shift at constant temperature have the same influence on infrared lens as thermal effect and its calculating model is correct.
There exist complex gray mapping relationships among infrared and visible images because of the different imaging mechanisms. The difficulty of infrared and visible image registration is to find a reasonable similarity definition. In this paper, we develop a novel image similarity called implicit line segment similarity(ILS) and a registration algorithm of infrared and visible images based on ILS. Essentially, the algorithm achieves image registration by aligning the corresponding line segment features in two images. First, we extract line segment features and record their coordinate positions in one of the images, and map these line segments into the second image based on the geometric transformation model. Then we iteratively maximize the degree of similarity between the line segment features and correspondence regions in the second image to obtain the model parameters. The advantage of doing this is no need directly measuring the gray similarity between the two images. We adopt a multi-resolution analysis method to calculate the model parameters from coarse to fine on Gaussian scale space. The geometric transformation parameters are finally obtained by the improved Powell algorithm. Comparative experiments demonstrate that the proposed algorithm can effectively achieve the automatic registration for infrared and visible images, and under considerable accuracy it makes a more significant improvement on computational efficiency and anti-noise ability than previously proposed algorithms.