Infrared Image Reconstruction Research Articles

Dual-branch feature encoding framework for infrared images super-resolution reconstruction

Infrared thermal imaging is a passive non-contact detection and identification technology, which is not subject to electromagnetic infection and good concealment, is widely used in military and commercial fields. However, due to the limitations of the existing infrared imaging system mechanisms, the spatial resolution of the acquired infrared images is low and the edge details are blurred, which in turn leads to poor performance in downstream missions based on infrared images. In this paper, in order to better solve the above problems, we propose a new super-resolution reconstruction framework for infrared images, called DBFE, which extracts and retains abundant structure and textual information for robust infrared image high-resolution reconstruction with a novel structure-textual encoder module. Extensive experiment demonstrates that our proposed method achieves significantly superior contraband high-resolution reconstruction results on the multiple dataset compared to progressive methods for high resolution infrared image reconstruction, effectively proving the practicability of the method proposed in this paper.

Blind infrared images reconstruction using covariogram regularization from a regular pentagon

Infrared images reconstruction is a fundamental problem in computer vision, which can remove the random image noise and produce sharp features. In this study, we propose a novel infrared image reconstruction model with covariogram regularization. Firstly, the covariogram of a regular pentagon is constructed. To enlarge the class of domains for which the forms of covariogram can be written explicitly, we reveal the explicit form of the covariogram for a regular pentagon in terms of the restricted chord function. As an application of the covariogram, the explicit expression of the containment function is achieved for a regular pentagon. Then, due to the non-convex property of the covariogram regularization, the fused coordinate descent framework is introduced to optimize the proposed image reconstruction model. To demonstrate the good performance of the proposed method, several real infrared image experiments are carried out. It shows that proposed method can smooth the random noise effectively and preserve the image structures.

Super-Resolution Based on Residual Learning and Optimized Phase Stretch Transform

High resolution infrared (IR) images are often required in military and industrial applications. Due to the limited properties of IR imaging sensors and camera lens, IR images exhibit poor spatial resolution with a blur phenomenon in the edge regions. In this correspondence, we develop a new super-resolution (SR)-IR image reconstruction method using the residual learning network in the wavelet domain (WRESNET) and optimized phase stretch transform (PST). Our algorithm first transforms the input low resolution (LR)-IR image into its low-frequency and high-frequency subbands using the discrete wavelet decomposition. Subsequently, we introduce the optimized PST to operate on the LR-IR image and extract the intrinsic edge structure. The PST behaves differently at low-frequency and high-frequency regions, thus capturing the intensity variations for edge detection. We incorporate the PST extracted edge map in the wavelet subbands to preserve the intrinsic structure of images. The resultant subbands are further refined based on the missing residuals obtained using the WRESNET. The proposed method is validated through quantitative and qualitative evaluations against the conventional and state-of-art SR methods. Results reveal that the proposed method outperforms the existing methods.

Infrared Image Reconstruction Based on Archimedes Spiral Measurement Matrix

It is a new research direction to realize infrared (IR) image reconstruction using compressed sensing (CS) theory. In the field of CS, the construction of measurement matrix is very principal. At present, the types of measurement matrices are mainly random and deterministic. The random measurement matrix can well satisfy the property of measurement matrix, but needs a large amount of storage space and has an inconvenient in hardware implementation. Therefore, a deterministic measurement matrix construction method is proposed for IR image reconstruction in this paper. Firstly, a series of points are collected on Archimedes spiral to construct a definite sequence; then the initial measurement matrix is constructed; finally, the deterministic measurement matrix is obtained according to the required sampling rate. Simulation results show that the IR image could be reconstructed by the measured values obtained through the proposed measurement matrix. Moreover, the proposed measurement matrix has better reconstruction performance compared with the Gaussian and Bernoulli random measurement matrices.

Edge preserving super-resolution infrared image reconstruction based on L1- and L2-norms

Super-resolution (SR) is a widely used technology that increases image resolution using algorithmic methods. However, preserving the local edge structure and visual quality in infrared (IR) SR images is challenging because of their disadvantages, such as lack of detail, poor contrast, and blurry edges. Traditional and advanced methods maintain the quantitative measures, but they mostly fail to preserve edge and visual quality. This paper proposes an algorithm based on high frequency layer features. This algorithm focuses on the IR image edge texture in the reconstruction process. Experimental results show that the mean gradient of the IR image reconstructed by the proposed algorithm increased by 1.5, 1.4, and 1.2 times than that of the traditional algorithm based on L1-norm, L2-norm, and traditional mixed norm, respectively. The peak signal-to-noise ratio, structural similarity index, and visual effect of the reconstructed image also improved.

An improved POCS super-resolution infrared image reconstruction algorithm based on visual mechanism

The traditional projection onto convex sets (POCS) super-resolution (SR) reconstruction algorithm can only get reconstructed images with poor contrast, low signal-to-noise ratio and blurring edges. In order to solve the above disadvantages, an improved POCS SR infrared image reconstruction algorithm based on visual mechanism is proposed, which introduces data consistency constraint with variable correction thresholds to highlight the target edges and filter out background noises; further, the algorithm introduces contrast constraint considering the resolving ability of human eyes into the traditional algorithm, enhancing the contrast of the image reconstructed adaptively. The experimental results show that the improved POCS algorithm can acquire high quality infrared images whose contrast, average gradient and peak signal to noise ratio are improved many times compared with traditional algorithm.

Fast multisensor infrared image super-resolution scheme with multiple regression models

We propose a fast super-resolution scheme with multiple regression models.This study combines the information from VI images and IR images.This study divides training patches into multiple clusters by K-means clustering.We propose a soft-assignment based multiple regression method. High resolution (HR) infrared (IR) images play an important role in many areas. However, it is difficult to obtain images at a desired resolution level because of the limitation of hardware and image environment. Therefore, improving the spatial resolution of infrared images has become more and more urgent. Methods based on sparse coding have been successfully used in single-image super-resolution (SR) reconstruction. However, the existing sparse representation-based SR method for infrared (IR) images usually encounter three problems. First, IR images always lack detailed information, which leads to unsatisfying IR image reconstruction results with conventional method. Second, the existing dictionary learning methods in SR aim at learning a universal and over-complete dictionary to represent various image structures. A large number of different structural patterns exist in an image, whereas one dictionary is not capable of capturing all of the different structures. Finally, the optimization for dictionary learning and image reconstruction requires a highly intensive computation, which restricts the practical application in real-time systems. To overcome these problems, we propose a fast IR image SR scheme. Firstly, we integrate the information from visible (VI) images and IR images to improve the resolution of IR images because images acquired by different sensors provide complementary information for the same scene. Second, we divide the training patches into several clusters, then the multiple dictionaries are learned for each cluster in order to provide each patch with a more accurate dictionary. Finally, we propose an method of Soft-assignment based Multiple Regression (SMR). SMR reconstructs the high resolution (HR) patch by the dictionaries corresponding to its K nearest training patch clusters. The method has a low level of computational complexity and may be readily suitable for real-time processing applications. Numerous experiments validate that this scheme brings better results in terms of quantization and visual perception than many state-of-the-art methods, while at the same time maintains a relatively low level of time complexity. Since the main computation of this scheme is matrix multiplication, it will be easily implemented in FPGA system.

Reconstruction algorithm of super-resolution infrared image based on human vision processing mechanism

Aiming at solving the problem of low resolution and visual blur in infrared imaging, a super-resolution infrared image reconstruction method using human vision processing mechanism (HVPM) was proposed. This method combined a mechanism of vision lateral inhibition with an algorithm projection onto convex sets (POCS) reconstruction, the improved vision lateral inhibition network was utilized to enhance the contrast between object and background of low-resolution image sequences, then POCS algorithm was adopted to reconstruct super-resolution image. Experimental results showed that the proposed method can significantly improve the visual effect of image, whose contrast and information entropy of reconstructed infrared images were improved by approximately 5 times and 1.6 times compared with traditional POCS reconstruction algorithm, respectively.

A Gabor subband decomposition ICA and MRF hybrid algorithm for infrared image reconstruction from subpixel shifted sequences

An image blind reconstruction, as a blind source separation problem, has been solved recently by independent component analysis (ICA). Based on ICA theory, in this paper, a high resolution image is reconstructed from low resolution and subpixel shifted sequences captured by infrared microscan imaging system. The algorithm has the attractive feature that neither the prior knowledge of the blur kernel nor the value of subpixel misregistrations between the input channels is required. The statistical independence in the image domain is improved by the multiscale Gabor subband decompositions, which are designed for the best ability to cover the whole spatial frequency and to avoid overlapping between the subbands. The mutual information is employed to locate a subband with the least dependent components. In terms of MAP estimator, we combine the super-Gaussian with Markov random field to form a hybrid image distribution. This strategy helps to estimate the separating matrix reasonable to extract the sources with the image properties, that is, sharp enough as well as correlative in local area. The proposed algorithm is capable of performing high resolution image sources which are not strictly independent, and its viability is proved by the computer simulations and real experiments.