FLIR Data Research Articles

Two adaptive enhancement algorithms for high gray-scale RAW infrared images based on multi-scale fusion and chromatographic remapping

As an important part of unmanned platform, the images taken by vehicle-mounted infrared camera are less affected by night, strong light and bad weather, which are widely used in the field of path planning and decision control in complex scenes. However, infrared images also have some shortcomings, such as high dynamic range, low contrast, high noise and blurred details, which make it difficult to enhance images and detect targets. In order to solve the above problems, two high grayscale infrared image enhancement algorithms were proposed in this paper. The MDDC_HDRII algorithm first proposed the idea of dehazing to solve the overexposure problem of the image, which adopted the method of multi-scale detail enhancement to improve the detail information of the image. PAEA_HDRII algorithm took the sensitivity of human vision as a reference factor, which made the image mostly in blue and red channels and enriched the detailed information of the image.Firstly, a high grayscale infrared image enhancement algorithm based on multi-scale fusion was proposed by combining the idea of debate and exposure fusion. Secondly, to solve the problems of poor visual effect and limited self-adaptation ability in the current pseudo-color ehancement algorithm, a pseudo-color enhancement algorithm for high gray-scale infrared images based on chromatographic remapping was proposed. Thirdly, qualitative and quantitative experiments were carried out by using the published FLIR data set. The results show that the two methods proposed in this paper can improve the overall quality of infrared images more effectively and highlight the details of the image. The objective image quality evaluation indexes are superior to most of the existing mainstream traditional algorithms. Then, two algorithms were used to enhance SAR images, and the experimental results show that this algorithm has good universality. Finally, the images enhanced by the two algorithms in this paper was sent to the YOLOv4 network for target detection, which improved the accuracy of target detection. The MDDC_HDRII algorithm first proposed the idea of dehazing to solve the overexposure problem of the image, which adopted the method of multi-scale detail enhancement to improve the detail information of the image. PAEA_HDRII algorithm took the sensitivity of human vision as a reference factor, which made the image mostly in blue and red channels and enriched the detailed information of the image.The algorithm can be applied in the image enhancement and target detection of vehicle-borne infrared images, SAR images, medical images and industrial film images, and provide reliable technical support for applications in related fields.

Infrared and visual image point set registration based on sensor parameters and refined CPD algorithm

In order to realize the FLIR and visual image registration effectively,an automatic registration algorithm based on sensor parameters and the refined CPD algorithm was proposed.Firstly,geometric rectification based on the attitude angle and height parameters was carried out to eliminate the rotation and scale discrepancies between the FLIR and visual images.Then the edges of visual image and rectified infrared image were extracted and a refined CPD algorithm was proposed for point set registration,the similarity transformation was adopted for fine image registration.Finally,the experiments on real FLIR data show that the proposed algorithm can realize the registration of infrared and visual images effectively and the registration precision can be around one pixel.

A temporal edge-based image segmentor

A general edge detection procedure is combined with new uses of temporal information in a segmentation algorithm for time-varying imagery. Edge detection is performed by thinning and thresholding the Prewitt gradient. Gaps in object boundaries are then filled by temporal information obtained by a multi-frame process of filtering, edge enhancement, and edge linking. The performance of the algorithm is tested by application to both FLIR and synthetic data. The FLIR data was collected by a sensor on a moving aerial platform recording a target that was also in motion. The synthetic data was created to model this type of complex motion in a controlled environment. The success of the method in segmenting target data and its advantages over conventional single frame techniques is discussed and illustrated.

Computational Cost of Image Registration with a Parallel Binary Array Processor

The application of a simulated binary array processor (BAP) to the rapid analysis of a sequence of images has been studied. Several algorithms have been developed which may be implemented on many existing parallel processing machines. The characteristic operations of a BAP are discussed and analyzed. A set of preprocessing algorithms are described which are designed to register two images of TV-type video data in real time. These algorithms illustrate the potential uses of a BAP and their cost is analyzed in detail. The results of applying these algorithms to FLIR data and to noisy optical data are given. An analysis of these algorithms illustrates the importance of an efficient global feature extraction hardware for image understanding applications.