Scene-based Algorithm Research Articles

Scene-based dual domain non-uniformity correction algorithm for stripe and optics-caused fixed pattern noise removal

Non-uniformity is a long-standing problem that significantly degrades infrared images through fixed pattern noise (FPN). Existing scene-based algorithms for non-uniformity correction (NUC) effectively eliminate stripe FPN assuming consistent inter-frame non-uniformity. However, they are ineffective in handling spatially continuous optical FPN. In this paper, a scene-based dual domain correction approach is proposed to address the non-uniformity problem by simultaneously removing stripe and optics-caused FPN. We achieve this through gain correction in the frequency domain and offset correction in the spatial domain. To remove stripes, we approximate the desired image using a guided filter and iteratively update the bias correction parameters frame by frame. For optics-caused noise removal, we separate low frequency noise from the scene using Fourier transform and update the gain correction parameters accordingly. To mitigate ghost artifacts, a combined strategy is introduced to adaptively adjusts learning rates and weights during the updating stage. Comprehensive evaluations demonstrate that our proposed approach outperforms compared methods in both real and simulated non-uniformity infrared videos.

Strong non-uniformity correction algorithm based on spectral shaping statistics and LMS.

The existence of non-uniformity in infrared detector output images is a widespread problem that significantly degrades image quality. Existing scene-based non-uniformity correction algorithms typically struggle to balance strong non-uniformity correction with scene adaptability. To address this issue, we propose a novel scene-based algorithm that leverages the frequency characteristics of the non-uniformity, combine and improve single-frame stripe removal, multi-scale statistics, and least mean square (LMS) methods. Following the "coarse-to-fine" correction process, the coarse correction stage introduces an adaptive progressive correction strategy based on Laplacian pyramids. By improving 1-D guided filtering and high-pass filtering to shape high-frequency sub-bands, non-uniformity can be well separated from the scene, effectively suppressing ghosting. In the fine correction stage, we optimize the expected image estimation and spatio-temporal adaptive learning rates based on guided filtering LMS method. To validate the efficacy of our algorithm, we conduct extensive simulation and real experiments, demonstrating its adaptability to various scene conditions and its effectiveness in correcting strong non-uniformity.

A registration based nonuniformity correction algorithm for infrared line scanner

A scene-based algorithm is developed for nonuniformity correction in focal plane of line scanning infrared imaging systems (LSIR) based on registration. By utilizing the 2D shift between consecutive frames, an implicit scheme is proposed to determine correction coefficients. All nonuniform biases are corrected to the same designated value, without estimating and removing biases explicitly, permitting quick computation for high-quality nonuniformity correction. Firstly, scene motion is estimated by image registration and consecutive frames exhibiting required 2D subpixel shift are collected. Secondly, we retrieve the difference matrix of adjacent biases by utilizing the 2D shift between consecutive frames. Thirdly, we perform specified elementary transformations and corresponding cumulative sums to the difference matrix to obtain a bias compensator. This bias compensator converts nonuniform biases to a designated detector’s bias. Finally, based on the different bias compensators obtained from several frame pairs, we calculate an averaged bias compensator for nonuniformity correction with less error. Quantitative comparisons with other nonuniformity correction methods demonstrate that the proposed algorithm achieves better fixed-pattern noise reduction with low computational complexity.

A New Scene-Based Non-Uniformity Correction Algorithm for Infrared Focal Plane Array

Non-uniformity exists in all IRFPA detectors. A new scene-based algorithm is developed to compensate for bias non-uniformity in focal-plane arrays. The technique is based on use of estimates of interframe sub-pixel shifts in an image sequence, in conjunction with a linear interpolation model for the motion, to exact information on the bias non-uniformity algebraically. Through a serial of experiment on real IRFPA images, we find the algorithm is effective and is more promising in comparison with the statistical approaches.

Scene-based nonuniformity correction for focal plane arrays by the method of the inverse covariance form.

What is to our knowledge a new scene-based algorithm for nonuniformity correction in infrared focal-plane array sensors has been developed. The technique is based on the inverse covariance form of the Kalman filter (KF), which has been reported previously and used in estimating the gain and bias of each detector in the array from scene data. The gain and the bias of each detector in the focal-plane array are assumed constant within a given sequence of frames, corresponding to a certain time and operational conditions, but they are allowed to randomly drift from one sequence to another following a discrete-time Gauss-Markov process. The inverse covariance form filter estimates the gain and the bias of each detector in the focal-plane array and optimally updates them as they drift in time. The estimation is performed with considerably higher computational efficiency than the equivalent KF. The ability of the algorithm in compensating for fixed-pattern noise in infrared imagery and in reducing the computational complexity is demonstrated by use of both simulated and real data.

Radiometrically accurate scene-based nonuniformity correction for array sensors.

A novel radiometrically accurate scene-based nonuniformity correction (NUC) algorithm is described. The technique combines absolute calibration with a recently reported algebraic scene-based NUC algorithm. The technique is based on the following principle: First, detectors that are along the perimeter of the focal-plane array are absolutely calibrated; then the calibration is transported to the remaining uncalibrated interior detectors through the application of the algebraic scene-based algorithm, which utilizes pairs of image frames exhibiting arbitrary global motion. The key advantage of this technique is that it can obtain radiometric accuracy during NUC without disrupting camera operation. Accurate estimates of the bias nonuniformity can be achieved with relatively few frames, which can be fewer than ten frame pairs. Advantages of this technique are discussed, and a thorough performance analysis is presented with use of simulated and real infrared imagery.

An algebraic algorithm for nonuniformity correction in focal-plane arrays.

A scene-based algorithm is developed to compensate for bias nonuniformity in focal-plane arrays. Nonuniformity can be extremely problematic, especially for mid- to far-infrared imaging systems. The technique is based on use of estimates of interframe subpixel shifts in an image sequence, in conjunction with a linear-interpolation model for the motion, to extract information on the bias nonuniformity algebraically. The performance of the proposed algorithm is analyzed by using real infrared and simulated data. One advantage of this technique is its simplicity; it requires relatively few frames to generate an effective correction matrix, thereby permitting the execution of frequent on-the-fly nonuniformity correction as drift occurs. Additionally, the performance is shown to exhibit considerable robustness with respect to lack of the common types of temporal and spatial irradiance diversity that are typically required by statistical scene-based nonuniformity correction techniques.