In this paper, a novel fractional-order fusion model (FFM) is presented for low-light image enhancement. Existing image enhancement methods don’t adequately extract contents from low-light areas, suppress noise, and preserve naturalness. To solve these problems, the main contributions of this paper are using fractional-order mask and the fusion framework to enhance the low-light image. Firstly, the fractional mask is utilized to extract illumination from the input image. Secondly, image exposure adjusts to visible the dark regions. Finally, the fusion approach adopts the extracting of more hidden contents from dim areas. Depending on the experimental results, the fractional-order differential is much better for preserving the visual appearance as compared to traditional integer-order methods. The FFM works well for images having complex or normal low-light conditions. It also shows a trade-off among contrast improvement, detail enhancement, and preservation of the natural feel of the image. Experimental results reveal that the proposed model achieves promising results, and extracts more invisible contents in dark areas. The qualitative and quantitative comparison of several recent and advance state-of-the-art algorithms shows that the proposed model is robust and efficient.

Images captured under varying light conditions have deficient contrast, low brightness, latent colors, and high noise. Numerous methods have been developed for image enhancement. However, these methods are only suitable for enhancing specific type of images (e.g., over-exposed or underexposed), and also fail to restore artifact-free results for various other types of images. Therefore, to meet this goal, in this paper, we present an automatic image enhancement method, which is capable of producing quality results for all types of images captured under uneven exposure conditions (e.g., backlit, non-uniform, over-exposed, one-sided illumination and night-time images). Firstly, images are categorized using a convolutional neural network (CNN) to determine their class, and different values of weight coefficients are achieved for further processing. Then, images are converted into photonegative form to obtain an initial transmission map using a bright channel prior. Next, L1-norm regularization is adopted to refine scene transmission. Besides, environmental light is estimated based on an effective filter. Finally, the image degradation model is applied to achieve enhanced results. Furthermore, post-processing of the images is comprised of two steps, such as denoising and details enhancement. The denoised model is only applied when the images are captured in extreme low-light conditions. Whereas, a smooth layer is obtained using L1-norm regularization to enhance details in partially over-and under-exposed images. Extensive experiments reveal the effectives of the proposed approach as compared to other state-of-the-art algorithms.

Owing to imaging equipment's environment and limitations, the images obtained in industrial cyber-physical systems (ICPSs) are degraded and available in various visual appearances. The process of highlighting the hidden contents in night-time contrast-distorted images is complex. Earlier approaches have solved this problem from a different perspective and achieved remarkable results that are generally unsatisfactory for images with diverse illumination distortions in ICPSs. Hence, an effective visibility enhancement model is proposed to eliminate inconsistent color casts while highlighting more hidden content for improved inspection, safety in large spaces, and monitoring of large systems. Our proposed model has four steps: 1) removal of the unnatural color cast via a white balance technique, 2) use of probability density and softplus functions to process the images with the actual color cast, 3) using an optimization algorithm to estimate the illumination and adjusting it using a nonlinear function, and 4) blending by multiscale fusion to obtain the most effective visual result. Evaluation of ten benchmark datasets using 14 quality metrics for 22 conventional and modern algorithms shows that our approach is robust, flexible, and applicable to numerous vision-based applications, such as ICPSs, autonomous vehicles, smart cameras, smart mobility, and transportation, especially in low-light environments.

Building detection in satellite images has been considered an essential field of research in remote sensing and computer vision. There are currently numerous techniques and algorithms used to achieve building detection performance. Different algorithms have been proposed to extract building objects from high-resolution satellite images with standard contrast. However, building detection from low-contrast satellite images to predict symmetrical findings as of past studies using normal contrast images is considered a challenging task and may play an integral role in a wide range of applications. Having received significant attention in recent years, this manuscript proposes a methodology to detect buildings from low-contrast satellite images. In an effort to enhance visualization of satellite images, in this study, first, the contrast of an image is optimized to represent all the information using singular value decomposition (SVD) based on the discrete wavelet transform (DWT). Second, a line-segment detection scheme is applied to accurately detect building line segments. Third, the detected line segments are hierarchically grouped to recognize the relationship of identified line segments, and the complete contours of the building are attained to obtain candidate rectangular buildings. In this paper, the results from the method above are compared with existing approaches based on high-resolution images with reasonable contrast. The proposed method achieves high performance thus yields more diversified and insightful results over conventional techniques.

The visual appearance of images changes in line with varying environmental light conditions. Adjusting the exposure of various distorted images is a highly complex process. Previous approaches have addressed this issue from different viewpoints and attained remarkable progress. However, they either failed to achieve visually pleasing results or were suitable for a single class of images (e.g., underexposed or nonuniform images). To fully consider the exposure in various distorted images, we proposed a diverse image enhancement model that improved the brightness and contrast, processed the colors, and eliminated the hazy effect. Accordingly, an input red green blue color image was transformed into a hue, saturation, value color image. The V component was inverted and enhanced using three steps. In the first step, the hyperbolic and statistical methods were applied, and then their results were combined using an adjusted logarithmic methodology. This method properly adjusted the high-contrast and low-contrast impact while preserving the vital image information. In the second step, the output of the first step was inverted back and fed into a complete optimization algorithm to estimate the illumination map. Then, the exposure ratio map was estimated using an illumination map, which was adjusted using the camera response function. In the third step, a nonlinear stretching function was introduced to control brightness and contrast. For instance, a lower value of α yielded maximum stretching, and a higher value of α eliminated haze in the image to a great extent. Finally, an empirical evaluation and comparison of the most recent state-of-the-art approaches on eight datasets revealed that the proposed model efficiently addressed the exposure in various degraded images.