Infrared Enhancement for Water Surface Imaging Based on Theory of Radiative Transfer and Edge Weight Analysis

Abstract

There always exist contrast reduction and details degradation when the infrared imaging system works on water surface because of fog and water mist. An image enhancement method is proposed based on radiative transfer theory. Firstly, the infrared enhancement model is built according to radiative transfer theory, with which we construct an optimization frame. Secondly, edge matrix is designed for weight analysis, which is particularly important for next estimation of fine transmissivity and optical path radiation. Finally, enhancement is achieved with edge matrix. The main contribution and novelty includes the derivation of enhancement based on radiative transfer theory, design of edge matrix, and estimation of fine transmissivity and optical path radiation. The experimental comparisons show the proposed method can generate results with a good visual effect, which is proved by both subjective and objective assessment. The experiments also indicate that the proposed algorithm runs fast to apply in real system.INDEX TERMS Infrared enhancement, theory of radiative transfer, weight analysis, water surface imaging.

Infrared imaging enhancement through local window‐based saliency extraction with spatial weight

“…To prove the effectiveness of the proposed algorithm, eight popular infrared enhancement approaches are selected for comparison, including, HE [6], radiative transfer and edge weight analysis (RTEWA) [14], CLAHE) [9], multi‐scale top‐hat transform (MSTH) [15], adaptive histogram partition and brightness correction approach (AHPBC) [11], local edge preserving filter method (LDPF) [13], and two deep learning‐based methods KinD [18] and Zero‐DCE [20]. Because of the lack of enhanced versions of low‐contrast infrared images, usually visible images are used for training for deep learning‐based methods.…”

“…Meanwhile, to verify the nine algorithms, six images are adopted just as shown in Figure 5, which are collected from open-source datasets, especially from [14,15,26] . Those images contain different potential targets of different sizes.…”

“…wileyonlinelibrary.com/iet-ipr radiative transfer theory is proposed [14]. Some multi-level or multi-scale-based spatial domain enhancement methods like the multi-scale top-hat transform (MSTH) method [15], multi-level image enhancement [16] and sub-band decomposition [17], all can produce results with good visual effect.…”

“…In LDPF, the authors use entropy‐guided gamma correction to adjust the base layer, but gamma correction may amplify some noise and make the image distorted. Moreover, to improve the visual effects of degradation, a water surface infrared image enhancement method based on radiative transfer theory is proposed [14]. Some multi‐level or multi‐scale‐based spatial domain enhancement methods like the multi‐scale top‐hat transform (MSTH) method [15], multi‐level image enhancement [16] and sub‐band decomposition [17], all can produce results with good visual effect.…”

Research on Performance Evaluation and Optimization Theory for Thermal Microscope Imaging Systems

“…The theoretical research of infrared imaging systems is the theoretical basis for the development of infrared imaging technology [11,12]. Among them, the performance evaluation theory can provide theoretical guidance for the design and optimization of the infrared imaging system [13,14]. MRTD (minimum resolvable temperature difference) and MDTD (minimum detectable temperature difference) have been important parameters for evaluating the performance of infrared imaging systems, mainly because they can quantify the temperature resolution capability of infrared imaging systems [15][16][17][18][19].…”