Images captured in low-light environments have low visibility and high noise, which will seriously affect subsequent visual tasks such as target detection and face recognition. Therefore, low-light image enhancement is of great significance in obtaining high-quality images and is a challenging problem in computer vision tasks. A low-light enhancement model, LLFormer, based on the Vision Transformer, uses axis-based multi-head self-attention and a cross-layer attention fusion mechanism to reduce the complexity and achieve feature extraction. This algorithm can enhance images well. However, the calculation of the attention mechanism is complex and the number of parameters is large, which limits the application of the model in practice. In response to this problem, a lightweight module, PoolFormer, is used to replace the attention module with spatial pooling, which can increase the parallelism of the network and greatly reduce the number of model parameters. To suppress image noise and improve visual effects, a new loss function is constructed for model optimization. The experiment results show that the proposed method not only reduces the number of parameters by 49%, but also performs better in terms of image detail restoration and noise suppression compared with the baseline model. On the LOL dataset, the PSNR and SSIM were 24.098dB and 0.8575 respectively. On the MIT-Adobe FiveK dataset, the PSNR and SSIM were 27.060dB and 0.9490. The evaluation results on the two datasets are better than the current mainstream low-light enhancement algorithms.

Recently, a lot of low-light image enhancement methods have been proposed. However, these methods have some problems such as causing fine details lost in bright regions and/or unnatural color tones. In this paper, we propose a new low-light image enhancement method to cope with these problems. In the proposed method, a pixel is represented by a convex combination of white, black, and pure color. Then, an equi-hue plane in RGB color space is represented as a triangle whose vertices correspond to white, black, and pure color. The visibility of low-light image is improved by applying a modified gamma transform to the combination coefficients on an equi-hue plane in RGB color space. The contrast of the image is enhanced by the histogram specification method using the histogram smoothed by a filter with a kernel determined based on a gamma distribution. In the experiments, the effectiveness of the proposed method is verified by the comparison with the state-of-the-art low-light image enhancement methods.

In order to solve the low-quality problems such as low brightness, poor contrast, noise interference and color imbalance in night images, a night image enhancement algorithm based on MDIFE-Net curve estimation is presented. This algorithm mainly consists of three parts: Firstly, we design an illumination estimation curve (IEC), which adjusts the pixel level of the low illumination image domain through a non-linear fitting function, maps to the enhanced image domain, and effectively eliminates the effect of illumination loss; Secondly, the DCE-Net is improved, replacing the original Relu activation function with a smoother Mish activation function, so that the parameters can be better updated; Finally, illumination estimation loss function, which combines image attributes with fidelity, is designed to drive the no-reference image enhancement, which preserves more image details while enhancing the night image. The experimental results show that our method can not only effectively improve the image contrast, but also make the details of the target more prominent, improve the visual quality of the image, and make the image achieve a better visual effect. Compared with four existing low illumination image enhancement algorithms, the NIQE and STD evaluation index values are better than other representative algorithms, verify the feasibility and validity of the algorithm, and verify the rationality and necessity of each component design through ablation experiments.

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.

LIME is a method for low-light image enhancement. Though LIME significantly enhances the contrast in dark regions, the effect of contrast enhancement tends to be insufficient in bright regions. In this letter, we propose an improved method of LIME. In the proposed method, the contrast in bright regions are improved while maintaining the contrast enhancement effect in dark regions.

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.

Light scattering and absorption of light in water cause underwater images to be poorly contrasted, haze and dominated by a single color cast. A solution to this is to find methods to improve the quality of the image that eventually leads to better visualization. We propose an integrated approach using Adaptive Gray World (AGW) and Differential Gray-Levels Histogram Equalization for Color Images (DHECI) to remove the color cast as well as improve the contrast and colorfulness of the underwater image. The AGW is an adaptive version of the GW method where apart from computing the global mean, the local mean of each channel of an image is taken into consideration and both are weighted before combining them. It is applied to remove the color cast, thereafter the DHECI is used to improve the contrast and colorfulness of the underwater image. The results of the proposed method are compared with seven state-of-the-art methods using qualitative and quantitative measures. The experimental results showed that in most cases the proposed method produced better quantitative scores than the compared 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.

This paper proposes a novel pseudo multi-exposure image fusion method based on a single image. Multi-exposure image fusion is used to produce images without saturation regions, by using photos with different exposures. However, it is difficult to take photos suited for the multi-exposure image fusion when we take a photo of dynamic scenes or record a video. In addition, the multi-exposure image fusion cannot be applied to existing images with a single exposure or videos. The proposed method enables us to produce pseudo multi-exposure images from a single image. To produce multi-exposure images, the proposed method utilizes the relationship between the exposure values and pixel values, which is obtained by assuming that a digital camera has a linear response function. Moreover, it is shown that the use of a local contrast enhancement method allows us to produce pseudo multi-exposure images with higher quality. Most of conventional multi-exposure image fusion methods are also applicable to the proposed multi-exposure images. Experimental results show the effectiveness of the proposed method by comparing the proposed one with conventional ones.

In this paper, we propose a new enhancement method for color images. In color image processing, hue preserving is required. The proposed method is performed into HSI color space whose gamut is same as RGB color space. The differential gray-level histogram equalization (DHE) is effective for gray scale images. The proposed method is an extension version of the DHE for color images, and furthermore, the enhancement degree is variable by introducing two parameters. Since our processing method is applied to not only intensity but also saturation, the contrast and the colorfulness of the output image can be varied. It is an important issue how to determine the two parameters. Thus, we give the guideline for how to decide the two parameters. By using the guideline, users can easily obtain their own enhancement images.

In photoacoustic imaging, laser power variation is one of the major factors in the degradation of the quality of reproduced images. A simple, but efficient method of compensating for the variations in laser pulse energy is proposed here where the characteristics of the adopted optical sensor and acoustic sensor were estimated in order to minimize the average local variation in optically homogeneous regions. Phantom experiments were carried out to validate the effectiveness of the proposed method.

Chen proposed an image quality assessment method to evaluate image quality at a ratio of noise in an image. However, Chen's method had some drawbacks that unnoticeable noise is reflected in the evaluation or noise position is not accurately detected. Therefore, in this paper, we propose a new image quality measurement scheme using the mean-centered WLNI (Weber's Law Noise Identifier) and the saliency map. The experimental results show that the proposed method outperforms Chen's and agrees more consistently with human visual judgment.

Absorption, scattering, and color distortion are three major issues in underwater optical imaging. Light rays traveling through water are scattered and absorbed according to their wavelength. Scattering is caused by large suspended particles that degrade underwater optical images. Color distortion occurs because different wavelengths are attenuated to different degrees in water; consequently, images of ambient underwater environments are dominated by a bluish tone. In the present paper, we propose a novel underwater imaging model that compensates for the attenuation discrepancy along the propagation path. In addition, we develop a fast weighted guided normalized convolution domain filtering algorithm for enhancing underwater optical images. The enhanced images are characterized by a reduced noise level, better exposure in dark regions, and improved global contrast, by which the finest details and edges are enhanced significantly.

Barrel distortion is a critical problem that can hinder the successful application of wide-angle cameras. This letter presents an implementation method for fast correction of the barrel distortion. In the proposed method, the required scaling factor is obtained by interpolating a mapping polynomial with a non-uniform spline instead of calculating it directly, which reduces the number of computations required for the distortion correction. This reduction in the number of computations leads to faster correction while maintaining quality: when compared to the conventional method, the reduction ratio of the correction time is about 89%, and the correction quality is 35.3 dB in terms of the average peak signal-to-noise ratio.

Recently, hue-preserving color image enhancement methods have been proposed by several researchers. However, the theoretical comparison of the performance of their methods has not been conducted yet. In this paper, we propose a hue-preserving saturation maximization method, and show a relationship of the saturation of enhanced colors by related methods. We also demonstrate the correctness of the relationship experimentally.

In this letter, we propose a novel Uniformity-Approximated Histogram Equalization (UAHE) algorithm to enhance the image as well as to preserve the image features. First, the UAHE algorithm generates the image histogram and computes the average value of all bins as the histogram threshold. In order to approximate the uniform histogram, the bins of image histograms greater than the above threshold are clipped, and the subtracted counts are averaged and uniformly assigned to the remaining bins lower than the threshold. The approximated uniform histogram is then applied to generate the intensity transformation function for image contrast enhancement. Experimental results show that our algorithm achieves the maximum entropy as well as the feature similarity values for image contrast enhancement.

We propose an unsharp-masking technique which preserves the hue of colors in images. This method magnifies the contrast of colors and spatially sharpens textures in images. The contrast magnification ratio is adaptively controlled. We show by experiments that this method enhances the color tone of photographs while keeping their perceptual scene depth.

We present an iterative method for inverse transform of nonlinear image processing. Its convergence is verified for image enhancement by an online software. We also show its application to amplification of the opacity in foggy or underwater images.

Histogram modification based image enhancement algorithms have been extensively used in 2-D image applications. In this letter, we apply a histogram modification framework to stereoscopic image enhancement. The proposed algorithm estimates the histogram of a stereo image pair without explicitly computing the pixel-wise disparity. Then, the histogram in the occluded regions is estimated and used to determine the target histogram of the stereo image. Experimental results demonstrate the effectiveness of the proposed algorithm.

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.