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Vision-based systems operating outdoors are significantly affected by weather conditions, notably those related to atmospheric turbidity. Accordingly, haze removal algorithms, actively being researched over the last decade, have come into use as a pre-processing step. Although numerous approaches have existed previously, an efficient method coupled with fast implementation is still in great demand. This paper proposes a single image haze removal algorithm with a corresponding hardware implementation for facilitating real-time processing. Contrary to methods that invert the physical model describing the formation of hazy images, the proposed approach mainly exploits computationally efficient image processing techniques such as detail enhancement, multiple-exposure image fusion, and adaptive tone remapping. Therefore, it possesses low computational complexity while achieving good performance compared to other state-of-the-art methods. Moreover, the low computational cost also brings about a compact hardware implementation capable of handling high-quality videos at an acceptable rate, that is, greater than 25 frames per second, as verified with a Field Programmable Gate Array chip. The software source code and datasets are available online for public use.

The existing most image processing methods based on physical models can have a significant impact on defogging performance due to inaccurate estimation of the depth of field information. These methods often encounter problems such as low brightness, invisible color distortion, and loss of detail when processing images with poor lighting conditions, such as those taken in coal mines. To address these issues, this paper proposes a new algorithm based on artificial multi-exposure image fusion. The proposed method performs global exposure on images with uneven illumination by combining S-type functions and the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm in the Hue-Saturation-Value (HSV) color space. This reduces the spatial dependence of brightness during processing and avoids color distortion problems that may arise in the Red-Green-Blue (RGB) color space. To mitigate the issue of detail loss, a gradient-domain guided filter is used to preserve fine structures in images, while an improved homomorphic filtering algorithm is introduced during the Laplacian pyramid decomposition process to reduce image content loss arising from large dark areas. This paper also conducted subjective, objective, and computational time comparisons to evaluate performance, providing reliable results regarding speed, quality, and reliability in processing hazy images.

Fringe projection profilometry (FPP) has been extensively applied in various fields for its superior fast speed, high accuracy and high data density. However, measuring objects with highly reflective surfaces or high dynamic range surfaces remains challenging when using FPP. A number of multiple exposure image fusion methods have been proposed and successfully improved measurement performance for these kinds of objects. Normally, these methods have a relatively fixed sequence of exposure settings determined by practical experiences or trial and error experiments, which may decrease the efficiency of the entire measurement process and may have less robustness with regard to various environmental lighting conditions and object reflective properties. In this paper, a novel self-adaptive multiple exposure image fusion method is proposed with two areas of improvement relating to adaptively optimizing the initial exposure and the exposure sequence. First, by introducing the theory of information entropy, combined with an analysis of the characterization of fringe image entropy, an adaptive initial exposure searching method is proposed. Then, an exposure sequence generation method based on dichotomy is further described. On the basis of these two improvements, a novel self-adaptive multiple exposure image fusion method for FPP as well as its detailed procedures are provided. Experimental results validate the performance of the proposed self-adaptivity multiple exposure image fusion method via the measurement of objects with differences in surface reflectivity under different ambient lighting conditions.

In applications such as satellite remote sensing and aerial photography, imaging equipment must capture brightness information of different ground scenes within a restricted dynamic range. Due to camera sensor limitations, captured images can represent only a portion of such information, which results in lower resolution and lower dynamic range compared with real scenes. Image super resolution (SR) and multiple-exposure image fusion (MEF) are commonly employed technologies to address these issues. Nonetheless, these two problems are often researched in separate directions. In this paper, we propose MEFSR-GAN: an end-to-end framework based on generative adversarial networks that simultaneously combines super-resolution and multiple-exposure fusion. MEFSR-GAN includes a generator and two discriminators. The generator network consists of two parallel sub-networks for under-exposure and over-exposure, each containing a feature extraction block (FEB), a super-resolution block (SRB), and several multiple-exposure feedback blocks (MEFBs). It processes low-resolution under- and over-exposed images to produce high-resolution high dynamic range (HDR) images. These images are evaluated by two discriminator networks, driving the generator to generate realistic high-resolution HDR outputs through multi-goal training. Extensive qualitative and quantitative experiments were conducted on the SICE dataset, yielding a PSNR of 24.821 and an SSIM of 0.896 for 2× upscaling. These results demonstrate that MEFSR-GAN outperforms existing methods in terms of both visual effects and objective evaluation metrics, thereby establishing itself as a state-of-the-art technology.

This paper aims to present a review of recent techniques in high dynamic range imaging (HDRI), which was the topic in research areas including image processing, computer graphics, and photography. HDRI or just HDR is a set of techniques that allows a greater dynamic range between the lightest and darkest areas of an image than current standard digital imaging techniques or photographic methods. HDR imaging technologies will spread its sphere of influence in imaging industry for its high quality and its powerful expression ability, including digital cinema, digital photography and next generation broadcast. This paper discusses recent advances, future direction in HDRI. The major goal of the paper is to provide a reference source for the researchers involved in HDRI, regardless of particular application areas.