When collecting images, owing to the influence of shooting equipment, shooting environment, and other factors, often low-illumination images with insufficient exposure are obtained. For low-illumination images, it is necessary to improve the contrast. In this paper, a digital color image contrast enhancement method based on luminance weight adjustment is proposed. This method improves the contrast of the image and maintains the detail and nature of the image. In the proposed method, the illumination of the histogram equalization image and the adaptive gamma correction with weighted distribution image are adjusted by the luminance weight of w1 to obtain a detailed image of the bright areas. Thereafter, the suppressed multi-scale retinex (MSR) is used to process the input image and obtain a detailed image of the dark areas. Finally, the luminance weight w2 is used to adjust the illumination component of the detailed images of the bright and dark areas, respectively, to obtain the output image. The experimental results show that the proposed method can enhance the details of the input image and avoid excessive enhancement of contrast, which maintains the naturalness of the input image well. Furthermore, we used the discrete entropy and lightness order error function to perform a numerical evaluation to verify the effectiveness of the proposed method.

A hybrid Retinex-based image enhancement algorithm is proposed to improve the quality of images captured by unmanned aerial vehicles (UAVs) in this paper. Hyperparameters of the employed multi-scale Retinex with chromaticity preservation (MSRCP) model are automatically tuned via a two-phase evolutionary computing algorithm. In the two-phase optimization algorithm, the Rao-2 algorithm is applied to performing the global search and a solution is obtained by maximizing the objective function. Next, the Nelder-Mead simplex method is used to improve the solution via local search. Real UAV-taken images of bad quality are collected to verify the performance of the proposed algorithm. Meanwhile, four famous image enhancement algorithms, Multi-Scale Retinex, Multi-Scale Retinex with Color Restoration, Automated Multi-Scale Retinex, and MSRCP are utilized as benchmarking methods. Meanwhile, two commonly used evolutionary computing algorithms, particle swarm optimization and flower pollination algorithm, are considered to verify the efficiency of the proposed method in tuning parameters of the MSRCP model. Experimental results demonstrate that the proposed method achieves the best performance compared with benchmarks and thus the proposed method is applicable for real UAV-based applications.

Multiscale retinex is one of the most popular image enhancement methods. However, its control parameters, such as Gaussian kernel sizes, gain, and offset, should be tuned carefully according to the image contents. In this letter, we propose a new method that optimizes the parameters using practical swarm optimization and multi-objective function. The method iteratively verifies the visual quality (i.e. brightness, contrast, and colorfulness) of the enhanced image using a multi-objective function while subtly adjusting the parameters. Experimental results shows that the proposed method achieves better image quality qualitatively and quantitatively compared with other image enhancement methods.

In recent years, much research interest has developed in image enhancement and haze removal techniques. With increasing demand for real time enhancement and haze removal, the need for efficient architecture incorporating both haze removal and enhancement is necessary. In this paper, we propose an architecture supporting both real-time Retinex-based image enhancement and haze removal, using a single module. Efficiently leveraging the similarity between Retinex-based image enhancement and haze removal algorithms, we have successfully proposed an architecture supporting both using a single module. The implementation results reveal that just 1% logic circuits overhead is required to support Retinex-based image enhancement in single mode and haze removal based on Retinex model. This reduction in computation complexity by using a single module reduces the processing and memory implications especially in mobile consumer electronics, as opposed to implementing them individually using different modules. Furthermore, we utilize image enhancement for transmission map estimation instead of soft matting, thereby avoiding further computation complexity which would affect our goal of realizing high frame-rate real time processing. Our FPGA implementation, operating at an optimum frequency of 125MHz with 5.67M total block memory bit size, supports WUXGA (1,920×1,200) 60fps as well as 1080p60 color input. Our proposed design is competitive with existing state-of-the-art designs. Our proposal is tailored to enhance consumer electronic such as on-board cameras, active surveillance intrusion detection systems, autonomous cars, mobile streaming systems and robotics with low processing and memory requirements.

The Retinex theory assumes that large intensity changes correspond to reflectance edges, while smoothly-varying regions are due to shading. Some algorithms based on the theory adopt simple thresholding schemes and achieve adequate results for reflectance estimation. In this paper, we present a practical reflectance estimation technique for hyperspectral images. Our method is realized simply by thresholding singular values of a matrix calculated from scaled pixel values. In the method, we estimate the reflectance image by measuring spectral similarity between two adjacent pixels. We demonstrate that our thresholding scheme effectively estimates the reflectance and outperforms the Retinex-based thresholding. In particular, our methods can precisely distinguish edges caused by reflectance change and shadows.

In this letter, we propose a method for obtaining a clear and natural output image by tuning the illumination component in an input image. The proposed method is based on the retinex process and it is suitable for the image quality improvement of images of which illumination is insufficient.

This paper describes an efficient image enhancement method based on the Multi-Scale Retinex (MSR) approach for pre-processing of video applications. The processing amount is drastically reduced to 4 orders less than that of the original MSR, and 1 order less than the latest fast MSR method. For the efficient processing, our proposed method employs multi-stage and multi-rate filter processing which is constructed by a x-y separable and polyphase structure. In addition, the MSR association is effectively implemented during the above multi-stage processing. The method also modifies a weighting function for enhancement to improve color rendition of bright areas in an image. A variety of evaluation results show that the performance of our simplified method is similar to those of the original MSR, in terms of visual perception, contrast enhancement effects, and hue changes. Moreover, experimental results show that pre-processing of the proposed method contributes to clear foreground object separation.

The purpose of this research is to propose an effective color metric which can predict the perceptual image quality for Retinex method. In this paper, we first give a brief introduction of three kinds of typical single Retinex methods to improve the color reproduction. And then, we state the process for obtaining the observer rating value from the subjective evaluation experiment performed under the sRGB illumination condition. Next, we introduce the S-CIELAB metric and propose a new metric on the basis of S-CIELAB metric that considers the gazing information. The average S-CIELAB color differences with and without the consideration of gazing information were calculated as the objective image quality measures. The correlations between the observer rating values and the objective image quality measures were calculated. The result shows that all of the average S-CIELAB color differences based on the gazing information are better correlated to the observer rating value than the average S-CIELAB color difference over the whole area. The average S-CIELAB color difference weighted by the gazing frequency over the gazing area shows the strong correlation with the observer rating value.