The non-uniformity is a time-dependent noise caused by the lack of sensor equalization. We present here the detailed algorithm and online demo of the non-uniformity correction method by midway infrared equalization. This method was designed to suit infrared images. Nevertheless, it can be applied to images produced for example by scanners, or by push-broom satellites. This single image method works on static images, is fully automatic, has no user parameter, and requires no registration. It needs no camera motion compensation, no closed aperture sensor equalization and is able to correct for a fully non-linear non-uniformity. Source Code The source code, version 2.0, is available from the article web page 1. The documentation is included in the archive. Basic compilation and usage instructions are included in the README.txt file. The source code for the contrast adjustment preprocessing is available from the same location with its own documentation (see [10]). The demo permits to try the proposed method on several well chosen test images, and on any uploaded image. To improve visibility (without changing the algorithm) the input image can be preprocessed using the "Simplest Color Balance" algorithm [10]. The s1 parameter is the percentage of pixels saturated to black and s2 the percentage saturated to white. If both s1 = s2 = 0 the image is simply stretched to [0, 255] by an affine contrast change (causing no loss of information) before applying the denoising algorithm. The output image is re-stretched to [0, 255] by an affine contrast change. The outputs are 1) the processed image and 2) the optimal scale parameter s found by the algorithm.
Modern IR cameras are increasingly equipped with built-in advanced (often non-linear) image and signal processing algorithms (like fusion, super-resolution, dynamic range compression etc.) which can tremendously influence performance characteristics. Traditional approaches to range performance modeling are of limited use for these types of equipment. Several groups have tried to overcome this problem by producing a variety of imagery to assess the impact of advanced signal and image processing. Mostly, this data was taken from classified targets and/ or using classified imager and is thus not suitable for comparison studies between different groups from government, industry and universities. To ameliorate this situation, NATO SET-140 has undertaken a systematic measurement campaign at the DGA technical proving ground in Angers, France, to produce an openly distributable data set suitable for the assessment of fusion, super-resolution, local contrast enhancement, dynamic range compression and image-based NUC algorithm performance. The imagery was recorded for different target / background settings, camera and/or object movements and temperature contrasts. MWIR, LWIR and Dual-band cameras were used for recording and were also thoroughly characterized in the lab. We present a selection of the data set together with examples of their use in the assessment of super-resolution and contrast enhancement algorithms.
This article analyzes and discusses a well-known paper [D. Li, R.M. Mersereau and S. Simske, IEEE Letters on Geoscience and Remote Sensing, 3:4 (2007), pp. 340-344] that applies principal component analysis in order to restore image sequences degraded by atmospheric turbulence. We propose a variant of this method and its ANSI C implementation. The proposed variant applies to image sequences acquired with short as well as long exposure times. Examples of restored images using sequences of real atmospheric turbulence are presented. The acquisition of a dataset of image sequences with real atmospheric turbulence is described and the dataset is made available for download.
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