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Due to the limitations of image-capturing devices or the presence of a non-ideal environment, the quality of digital images may get degraded. In spite of much advancement in imaging science, captured images do not always fulfill users’ expectations of clear and soothing views. Most of the existing methods mainly focus on either global or local enhancement that might not be suitable for all types of images. These methods do not consider the nature of the image, whereas different types of degraded images may demand different types of treatments. Hence, we classify images into several classes based on the statistical information of the respective images. Afterwards, an adaptive gamma correction (AGC) is proposed to appropriately enhance the contrast of the image where the parameters of AGC are set dynamically based on the image information. Extensive experiments along with qualitative and quantitative evaluations show that the performance of AGC is better than other state-of-the-art techniques.

We proposed the Retinex-based fast algorithm (RBFA) to achieve low-light image enhancement in this paper, which can restore information that is covered by low illuminance. The proposed algorithm consists of the following parts. Firstly, we convert the low-light image from the RGB (red, green, blue) color space to the HSV (hue, saturation, value) color space and use the linear function to stretch the original gray level dynamic range of the V component. Then, we estimate the illumination image via adaptive gamma correction and use the Retinex model to achieve the brightness enhancement. After that, we further stretch the gray level dynamic range to avoid low image contrast. Finally, we design another mapping function to achieve color saturation correction and convert the enhanced image from the HSV color space to the RGB color space after which we can obtain the clear image. The experimental results show that the enhanced images with the proposed method have better qualitative and quantitative evaluations and lower computational complexity than other state-of-the-art methods.

In this paper, a synthetic aperture radar (SAR) change detection approach is proposed based on a structural similarity index measure (SSIM) and multiple-window processing (MWP). The proposed scheme is performed in two steps: (1) generation of a coherence image based on MWP associated with SSIM and (2) gamma correction (GC) filtering. The proposed method is capable of providing a high-quality coherence image because the MWP operation based on SSIM has high sensitivity to the similarity measure for intensity between two SAR images. By finding an optimum value of order of GC, the proposed method can considerably reduce the effect of speckle noise on the coherence image, while retaining nearly all the information related to changed region involved in the change detection map. Several experimental results are presented to demonstrate the effectiveness of the proposed scheme.

In sand-dust environments, the low quality of images captured outdoors adversely affects many remote-based image processing and computer vision systems, because of severe color casts, low contrast, and poor visibility of sand-dust images. In such cases, conventional color correction methods do not guarantee appropriate performance in outdoor computer vision applications. In this paper, we present a novel color correction and dehazing algorithm for sand-dust image enhancement. First, we propose an effective color correction method that preserves the consistency of the chromatic variances and maintains the coincidence of the chromatic means. Next, a transmission map for image dehazing is estimated using the gamma correction for the enhancement of color-corrected sand-dust images. Finally, a cross-correlation-based chromatic histogram shift algorithm is proposed to reduce the reddish artifacts in the enhanced images. We performed extensive experiments for various sand-dust images and compared the performance of the proposed method to that of several existing state-of-the-art enhancement methods. The simulation results indicated that the proposed enhancement scheme outperforms the existing approaches in terms of both subjective and objective qualities.

Low-contrast or uneven illumination in real-world images will cause a loss of details and increase the difficulty of pattern recognition. An automatic image illumination perception and adaptive correction algorithm, termed as GLAGC, is proposed in this paper. Based on Retinex theory, the illumination of an image is extracted through the discrete wavelet transform. Two features that characterize the image illuminance are creatively designed. The first feature is the spatial luminance distribution feature, which is applied to the adaptive gamma correction of local uneven lighting. The other feature is the global statistical luminance feature. Through a training set containing images with various illuminance conditions, the relationship between the image exposure level and the feature is estimated under the maximum entropy criterion. It is used to perform adaptive gamma correction on global low illumination. Moreover, smoothness preservation is performed in the high-frequency subband to preserve edge smoothness. To eliminate low-illumination noise after wavelet reconstruction, the adaptive stabilization factor is derived. Experimental results demonstrate the effectiveness of the proposed algorithm. By comparison, the proposed method yields comparable or better results than the state-of-art methods in terms of efficiency and quality.

In experimental color research, one must ensure that color is displayed and described reliably. When monitors are involved, colors are displayed through device-dependent color systems. However, these values must be translated into device-independent color systems to interpret what people perceive, often involving techniques such as gamma correction. We sought to explore the feasibility of estimating gamma instead of relying on direct gamma measurements, which typically require specialized equipment like a chromameter. Potential solutions include a computerized perception-based gamma estimation task or adopting the industry-standard gamma value of 2.2. We compared these two solutions against the chromameter measurements in the context of a color-matching task. Thirty-nine participants visually matched red, yellow, green, and blue physical objects using a computerized color picker. Starting from these color choices, we applied two RGB-to-CIELab color conversion methods: one using a perception-based gamma estimation and another using the industry-standard gamma. Color values obtained with the chromameter differed from the other two methods by 6–15 JNDs. Small differences existed between the results obtained using the perception-based task and the industry-standard gamma. Thus, we conclude that when standard viewing conditions cannot be assumed, adopting a gamma value of 2.2 should suffice.

We present a 640 × 480 CMOS image sensor (CIS) with in-circuit bi-directional gamma correction with a proposed digital-correlated double sampling (CDS) structure. To operate the gamma correction in the CIS, the transfer function of the analog-to-digital converter can be changed by controlling the clock frequency of the counter using analog CDS. However, the analog CDS is vulnerable to capacitor mismatch, clock feedthrough, etc. Therefore, we propose a digital-CDS method with a hold-and-go counter structure to operate the bi-directional gamma correction in the CIS. The proposed CIS achieves a 10-bit resolution using a global log-exponential counter and configurable column reset counter with a resolution of 8/9 bits. The sensor was fabricated in a 0.11 μm CIS process, and the full chip area was 5.9 mm × 5.24 mm. The measurement results showed a maximum SNR improvement of 10.41% with the proposed bi-directional gamma-corrected digital-CDS with the hold-and-go counter. The total power consumption was 6.3 mW at a rate of 16.6 frames per second with analog, pixel, and digital supply voltages of 3.3 V, 3.3 V, and 1.5 V, respectively.

Underwater images typically suffer from poor visibility, low contrast, and severe color distortion caused by wavelength-dependent absorption and scattering of light. These degradations not only reduce visual quality but also affect subsequent analysis and interpretation in marine and robotic imaging applications. To address these challenges, this study presents an efficient underwater image enhancement (UIE) framework that integrates color balancing, morphological residual processing, and gamma correction to achieve natural color restoration and structural enhancement. Initially, an adaptive color compensation strategy corrects the imbalance in red and blue channels, followed by morphological residual processing that refines fine textures while suppressing unwanted noise. The enhanced outputs are then fused through an adaptive multiscale fusion process guided by optimized weight maps to preserve both global illumination and local detail. A final gamma correction step ensures perceptually balanced contrast and brightness. The proposed method requires no training data or prior depth estimation making it computationally efficient and robust for real-time applications. Extensive experiments conducted on multiple benchmark underwater datasets demonstrate that the proposed approach consistently outperforms 22 state-of-the-art UIE techniques in both qualitative and quantitative assessments. The method achieves superior results in terms of peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), underwater image quality measure (UIQM), and underwater color image quality evaluation (UCIQE) metrics, confirming its capability to restore realistic colors, enhance visibility, and preserve fine details. The proposed framework provides an effective and lightweight solution for practical underwater imaging enhancement. This work supports SDG 14 (Life Below Water) by enhancing underwater imagery for marine monitoring, SDG 9 (Industry, Innovation and Infrastructure) through an efficient real-time imaging framework, and SDG 12 (Responsible Consumption and Production) by enabling accurate underwater inspection that promotes sustainable resource use.

Images captured by cameras in low-light conditions have low quality and appear dark due to insufficient light exposure, which critically affects the view. Most of the traditional enhancement methods are based on the entire image for exposure enhancement, so overexposed areas in the image have the risk of secondary enhancement. In order to fully consider the exposure in low-light images, we propose a low-light image enhancement based on multi-illumination estimation, which can robustly produce high-quality results for various underexposures. The core of the proposed method is to derive multiple exposure correction images using light estimation. Then, we used a Laplacian multi-scale fusion method to combine the weight map and the images with different degrees of exposure. We used gamma correction and inversion on the original image to produce images with different exposure levels (such as underexposure, overexposure, and partial area overexposure and underexposure). The gamma-corrected image is used for lighting adjustment of underexposed areas in low-light images, while the inversion image is used for adjustment of the overexposed regions. We performed experiments on various images using multiple methods and evaluated and compared the experimental results, qualitatively and quantitatively. Experimental results show that the proposed method in this study can effectively eliminate the effects of low light and improve image quality.

Image enhancement is an essential step in image analysis and processing as it helps people to recognize and understand images because their perception is greatly influenced by image quality. Incomplete beta function (IBF) is a broadly employed transformation function for image contrast enhancement (ICE). However, IBF has low parameter selection efficiency, a limited range of mutable parameters to stretch areas with high or low gray levels, and image enhancement with stretching at both ends is almost ineffectual. In this paper, a hybrid whale optimization algorithm (WOA) with the Chameleon Swarm algorithm (CSA), referred to as HWOA, is presented to adaptively determine the optimal parameters of IBF for ICE. Then, bilateral gamma correction (BGC) is utilized to produce better contrast and brightness while preserving edge detail. The proposed HWOA algorithm follows a multi-phased process of strategies. Many improvements were made to the mathematical model of WOA followed by its hybridization with CSA for further exploration and exploitation aspects. The proposed HWOA is tested over some standard images along with well-known available Kodak image dataset and assessed using several standard measures. The experimental results showed that the proposed algorithm can satisfactorily surpass many other algorithms that used the same image enhancement approach as well as other traditional image enhancement methods deemed here for comparison. Specifically, the findings on ten color images revealed that the performance of HWOA in terms of average peak signal-to-noise ratio, average structural similarity index, and average values of entropy results are more than 33.0, 96.6%, and 7.3, respectively, and these results are much better than all other comparative methods in the corresponding evaluation criteria.