Underwater images often suffer from visual degradation due to varying light absorption at different wavelengths and scattering from suspended particles. To tackle these issues, we present an intelligent optimized multi-exposure image fusion method called IMIF. Specifically, we propose an adaptive color transfer strategy that employs a colorless reference image to correct the color distortion issue by transferring the mean and standard deviation of the reference image to adjust a color-balanced image. Subsequently, we introduce a particle swarm optimization algorithm that intelligently selects the optimal set of exposure image sequences by employing information entropy and edge intensity of the image as fitness metrics. Meanwhile, we leverage a guided filtering strategy to decompose the exposure image sequences into basic and detailed layers, taking into account the exposure characteristics of each layer to generate corresponding weight maps. Finally, we employ a multi-exposure fusion strategy to adaptively fuse the exposed image sequences with weight maps, producing an enhanced result. Extensive experiments conducted on three datasets demonstrate that our IMIF method outperforms state-of-the-art (SOTA) methods in both qualitative and quantitative evaluations. Additionally, the enhanced results produced by our proposed IMIF method significantly improve the accuracy of object detection and keypoint detection. The is available at https://www.researchgate.net/publication/403951386_2026-IMIF.

Hyperspectral remote sensing plays an important role in agricultural monitoring, and fine-grained wheat variety classification is essential for advancing smart agriculture. However, progress is limited by the scarcity of high-quality spectral samples and the challenges associated with collecting large-scale hyperspectral data. To cope with these issues, we design a hybrid supervised-driven two-stream collaborative network (HSTNet), which consists of a semi-supervised conditional generative adversarial network for data augmentation (SCGAN) and a supervised two-stream discriminative network (STDNet) for wheat classification. In SCGAN, the generator constructs a mapping relationship between input noise and real wheat hyperspectral samples to generate fake wheat hyperspectral samples that are highly matched with the distribution of the real sample, and the discriminator with multilayer perceptions utilizes discriminative learning to identify real and fake samples. In STDNet, it collaboratively extracts the spectral, spatial and texture features of wheat hyperspectral images employing the dual-stream branch structure of 3DCNN and 2DCNN. Subsequently, it utilizes the Fast Fourier Transform and the cross-attention mechanism to refine and fuse these features to improve their capability of feature expression. Noteworthy, the individual design effectively improves the classification results of wheat varieties via collaborative optimization among modules. Besides, we built a mixed wheat hyperspectral dataset (MWHD) with 4800 samples of 20 wheat varieties. Extensive experiments on our constructed MWHD dataset demonstrate that the proposed HSTNet outperforms state-of-the-art methods in wheat variety classification. The code is publicly available at: https://github.com/bakam412/HSTNet.

Due to the complex imaging mechanism, underwater images often suffer from multiple degradation issues, such as color cast, blurry detail, and low contrast, which affect the extraction of valuable information. To deal with these degradation issues, a simple yet effective underwater image quality improvement method based on color, detail and contrast restoration (CDCR) is developed, which consists of three key modules: a well-preserved finding-driven color balance module (CBM), a linear saturation transformation-based discriminant function-based detail restoration module (DRM), and a transmission minimization-oriented contrast restoration module (CRM). First, the CBM explores a well-preserved channel finding and employs a channel compensation strategy to balance the color differences among three color channels. Second, the DRM uses a piecewise underwater image saturation estimation strategy, which takes the various spectral properties of water into account and designs an additional linear saturation transformation-based discriminant function to prevent the transmission from being under-estimated. At last, the CRM estimates a global backscatter light based on transmission minimization and further improves the contrast by locally removing the backscatter light of the base layer. Our restored image is appealing in its natural color, fine details, and high contrast. Extensive experiments on three underwater image enhancement datasets show that our CDCR achieves better results than state-of-the-art methods, i.e., compared with the second-best method, the average PCQI and UIQM values of our method increase by 5.7% and 0.2%, and the average Blur and DFAD values of our method decrease by 8.0% and 5.3%. Meanwhile, experiments further suggest that the rate of new visible edges and the quality of contrast restoration of our CDCR at least increase by 7.7% and 51.2% in most tested sandstorm and foggy images, respectively, which demonstrates that our method has a good generalization capability for sandstorm and foggy image restoration.

Remote sensing images are significantly degraded by bad weather conditions, such as haze and sandstorms, which provide unhelpful support for valuable information extraction. Most existing remote sensing image enhancement methods ignore the wavelength dependence of the scattering coefficient and local scattering differences of images, and therefore cannot well handle the colorized haze in which the medium transmission varies in different color channels. In this article, we propose a single image quality enhancement method using joint local structure dehazing and local texture enhancement (SDTE). Specifically, SDTE first uses a minimal channel between r, g, and b channels to estimate a coarse local airlight, and designs an achromatic airlight-driven refinement strategy to refine it. Meanwhile, SDTE estimates a local transmission via independent calculation of r, g, and b channels, which tackles the limitation that existing methods heavily depend on global transmission over the entire image. Then, SDTE removes the haze and amplifies the gradient using the estimated local airlight and transmission, thereby preserving significant structures and enhancing fine details. Finally, SDTE introduces an adaptive color correction based on the ranking of channel mean value and two channel-dependent gain factors to further eliminate the severe color distortion. More specially, we also collect a remote sensing colorized hazy image enhancement benchmark (RSCHI) including 339 remote sensing images captured in colorized haze or sandstorm, which makes it pay more attention to the color cast issue. We conduct a comprehensive study on benchmark datasets of RSCHI and UIEB and indicate better performance than the state-of-the-art (SOTA) methods. Meanwhile, we use a series of ablation studies to demonstrate the effectiveness and robustness of each key contribution and validate its generalization performance in other scenes.

This letter proposes an effective underwater image restoration method that consists of a local image descattering and an adaptive brightness improvement. First, we establish an adaptive objective function for improving the brightness of underwater image according to the best-preserved channel of an image, and an augmented Lagrange multiplier based alternating direction minimization algorithm is derived to solve the optimization problem. Second, we introduce a local transmission estimation method that takes into account the different attenuation of light on the red, green and blue channels, which overcomes the limitation that existing methods heavily depend on the global transmission over the entire image. Extensive experiments on real-world underwater images demonstrate the effectiveness of the proposed method in underwater image restoration. Moreover, our method shows good generalization capability for enhancing remote sensing and nighttime images.

Due to the absorption and scattering of light, underwater captured images often face serious quality degradation issues. In this letter, we propose to cope with the aforementioned issues via piecewise color correction and dual prior optimized contrast enhancement. Specifically, we first present the piecewise color correction method using the maximum mean and two gain factors to correct the color cast of each color channel. Then, we propose a dual prior optimized contrast enhancement method, which relies on the spatial and texture priors to decompose the base layer and detail layer of the V channel in HSV color space. Meanwhile, we employ different enhancement strategies in different layers to enhance the contrast and texture detail of underwater images. Our extensive experiments on several benchmark datasets show that our method outperforms eleven compared state-of-the-art methods. Moreover, our method has good generalization capability for fog and low-light images. The code is available athttps://github.com/Li-Chongyi/PCDE.

Underwater images usually suffer from color casts and low contrast due to the absorption and scattering of light by the water medium. The degradation is caused not only by the light attenuation on the scene-sensor path but also by the light attenuation on the water surface-scene path. To eliminate the dual-path light attenuation, we propose a novel unified total variation method based on an extended underwater imaging model. Unlike previous variation-based methods that only consider light propagation along the scene-sensor path, we additionally include light propagation along the water surface-scene path in the underwater imaging model. In the proposed variational framework, we transform underwater image enhancement into two subproblems and construct different prior knowledge-guided optimization functions for them. The two subproblems aim to remove the light attenuation along the scene-sensor and surface-scene paths. Moreover, we present an alternating direction minimization algorithm based on an augmented Lagrange multiplier to address the optimization problems. The subjective and objective experimental results on underwater images with different attenuation characteristics demonstrate that the proposed method achieves good performance in underwater image enhancement.

In this letter, a simple yet effective dehazing framework is proposed, which consists of a novel color correction and a contrast enhancement. Most of the existing dehazing works focus on enhancing the contrast of the degraded images, but rarely of them concern about the color cast, which is ubiquitous in the scattering medium. To address the color distortion, an affine transform model-based color correction method is first proposed to improve the appearance of the image while preserving the details, which is inspired by the traditional color transfer. The color transfer alters the color values of a source image by sharing the global color statistics of a reference image, which makes it unsuitable to address the locally variable color deviations encountered in highly color distorted images as in poor visibility conditions (sandstorms and underwater). To alter color correction locally, we add local color fidelity and gradient constraint to the proposed technique, which overcomes the limitation that the traditional method depends too much on the global color statistics of the reference image and encourages it to handle the degraded image with various color casts and light conditions. In addition, a multiscale gradient-domain processing is applied to enhance the contrast. In this procedure, by extracting the information of different layers, we can easily restore the contrast while limiting the significant amplification of noise. The extensive qualitative and quantitative experiments reveal that the color and the contrast can be significantly improved by the proposed technique.

Image taken in turbid media generally exists poor visibility and low contrast, which results from attenuation of the propagated light. In this letter, an effective polarization-based image dehazing method is proposed, which relies on the relationship between the angle of polarization (AoP) from the Stokes vector and the scattered light. To avoid the influence of noise, AoP is optimized based on regularization constraints. The regularization function is made using an assumption that adjacent pixels with similar colors have similar values of AoP. Moreover, according to the revised AoP information, all the key parameters can be effectively and automatically estimated without considering the no-object region (or the sky region) exists or not, which relies on a frequency prior strategy. Extensive experiments on real-world images demonstrate that the proposed method is more effective than several previous image restoration or enhancement works.

Images captured under low-light conditions are generally characterized by poor illumination, low contrast, and nonignorable large amount of noise. In order to improve the visibility in weak illumination scenes, multiple artificial light sources are used, which leads to severe uneven illumination of the scene. The main challenges of dehazing images with complex illumination are to suppress the boosting of unsightly noise when enhancing contrast and avoid overenhancement in bright glow regions. To circumvent problems above, this letter proposes a generalized enhancement framework, which works well not only in uniform light conditions but also in strongly nonuniform illumination low-light scenes. To achieve this, we first decompose the input hazy image into a structure layer containing low-frequency illumination variance and a texture layer containing large amount of high-frequency details. Sequentially, benefit from two derived masks that are intrinsically similar to weight maps, the proposed framework can perform regional adaptive brightness adjustment on the structure layer according to the distribution of light in the input image. Meanwhile, regions of effective details in the texture layer are assigned higher weights, while regions that belong to noise are suppressed. Finally, adding the enhanced texture layer back to the brightened structure layer, visually appealing results are generated. Experimental results on various scenarios demonstrate the superiority of the proposed framework over state-of-the-art methods in terms of both qualitative and quantitative.

This letter introduces an underwater image enhancement method to handle low contrast and color cast of underwater images. Firstly, with the help of hierarchical searching technique, we propose a novel backscattered light estimation method. And in this procedure, a novel scoring formula is considered into our method, which comprehensively considers multiple prior knowledge. Then, we generalize underwater dark channel prior (UDCP) approach to obtain more robust transmission estimation. In addition, we also develop a white balance method to further modify the appearance of the resultant image. Extensive experiments on real-world images demonstrate that the proposed method outperforms several previous image restoration or enhancement works.

Recently, image restoration has attracted considerable attention from researchers, and these methods generally restore degraded images based on the atmospheric scattering model (ATSM) and retinex model (RM). The two models only take into the single attenuation process during imaging, thereby introducing undesirable results. To deal with this issue, we propose an image restoration method based on a generalized image formation model (GIFM). First, unlike the existing image restoration methods, we rebuild a novel image formation model, which describes the light attenuation process that includes the light source-scene path and scene-sensor path. Second, we construct an objective optimization function to decompose a degraded image into a color distorted component and color corrected component, and an augmented Lagrange multiplier-based alternating direction minimization algorithm is provided to solve the optimization problem. Finally, we fully consider the advantages of the small-scale neighborhood and large-scale neighborhood in image restoration, and an image itself brightness-based weighted fusion strategy is proposed to balance brightness enhancement and contrast improvement. Extensive experiments on three image enhancement datasets show that our GIFM achieves better results than state-of-the-art methods. Experiments further suggest that our GIFM performs well for image restoration of extreme scenes, keypoint detection, object detection, and image segmentation.