Abstract
A reference-free image index to jointly assess infrared-imaging fixed-pattern-noise and blur artifacts is proposed in this work. The proposed index is based on tuned-spatial-domain filtering, which works by combining two Laplace operators to simultaneously quantify the global infrared-imaging fixed-pattern-noise and the global or local blur artifacts. The index effectiveness is demonstrated by two task-based image-quality assessments to determine the focused and fixed-pattern-noise free images from sequences captured with both a mid-wave-infrared microscope system and a long-wave-infrared plenoptic system. The index quantitative limits are shown on numerical computations over synthetic corrupted images as well as real black-body radiator calibrated infrared images with representative simulated fixed-pattern noise, from six well known infrared focal plane arrays transducer technologies, along with artificial blur added using real infrared imaging system point spread functions.
Highlights
For an image quality method to be useful, it has to objectively quantify the usefulness of an image to perform a given task
IR focal plane array (FPA) TECHNOLOGIES The proposed roughness Laplacian pattern (RLP) index is tested with images from a CEDIP Jade-UC long-wavelength infrared (LWIR) camera, with a FPA based on an uncooled microbolometer detector with 320 × 240 pixels, spectral response between 8–12 [μm], and 14-bits digital output
The metric is tested with a synthetic target formed by an array of images, where the initial image is assembled with various forms of pixel intensity variations, the array is completed increasing fixed-pattern noise (FPN), and augmenting blur
Summary
For an image quality method to be useful, it has to objectively quantify the usefulness of an image to perform a given task. A. Jara et al.: Reference-Free Image Index integration operations are still yielding raw IR images with poor signal-to-noise ratio (SNR), which can be even less than one for most applications. Jara et al.: Reference-Free Image Index integration operations are still yielding raw IR images with poor signal-to-noise ratio (SNR), which can be even less than one for most applications This happens since, during the IR radiance integration process, the digital raw data turns out to be mainly degraded by image dispersion and opto-electronics fixed-pattern noise (FPN), being the severity and nature of both IR imaging artifacts, application and transducer technology dependent [6], [16]–[18]. This article proposes a index to simultaneously quantify the two main degrading components presented in several applications of IR imaging systems: the FPN and blurring artifacts.
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