The underwater environment is complex and ever-changing, and color distortion and reduced contrast are the two primary issues encountered in original underwater images. This article applies image enhancement to address these concerns by proposing a novel image color correction red channel correction after gray world (RAG) algorithm and an improved multiscale image fusion (IMF) algorithm for each problem. In terms of color correction, traditional algorithms typically implement a single color correction step, leading to issues such as locally unrealistic colors and limited applicability in certain scenes. The proposed RAG algorithm advances upon these methods by further dividing the input image into multiple local blocks after performing global color correction using the gray world algorithm. Secondary color corrections are then applied within each local block, overcoming the limitations of the traditional single-step color correction process. The corrected images achieve an underwater image colorfulness measure (UICM) mean value of 5.4671, representing an enhancement exceeding 20%. In contrast, the existing fusion algorithms often neglect the distinct characteristics of different channels and fail to maximize the advantages of fusion. The IMF algorithm proposed in this article calculates weights in the red green blue color space (RGB) and LAB color spaces of images to effectively incorporate channel disparities, refining the weights to provide a more thorough fusion process. The corrected images achieve a mean underwater color image quality evaluation (UCIQE) value of 0.630 and a mean colorfulness, contrast and fogdensity (CCF) value of 37.495. Significant improvements are observed across various other metrics.

Meeting the escalating demand for high-quality underwater imagery poses a significant challenge due to light absorption and scattering in water, resulting in color distortion and reduced contrast. This study presents an innovative approach for enhancing underwater images, combining color correction, HSV color space equalization, and multi-scale fusion techniques. Initially, automatic contrast adjustment and improved white balance corrected color bias; this was followed by saturation and value equalization in the HSV space to enhance brightness and saturation. Gaussian and Laplacian pyramid methods extracted multi-scale features that were fused to augment image details and edges. Extensive subjective and objective evaluations compared our method with existing algorithms, demonstrating its superior performance in UCIQE (0.64368) and information entropy (7.8041) metrics. The proposed method effectively improves overall image quality, mitigates color bias, and enhances brightness and saturation.

Large-aperture, lightweight, and high-resolution imaging are hallmarks of major optical systems. To eliminate aberrations, traditional systems are often bulky and complex, whereas the small volume and light weight of diffractive lenses position them as potential substitutes. However, their inherent diffraction mechanism leads to severe dispersion, which limits their application in wide spectral bands. Addressing the dispersion issue in diffractive lenses, we propose a chromatic aberration correction algorithm based on compressed sensing. Utilizing the diffractive lens’s focusing ability at the reference wavelength and its degradation performance at other wavelengths, we employ compressed sensing to reconstruct images from incomplete image information. In this work, we design a harmonic diffractive lens with a diffractive order of $M=150$, an aperture of 40 mm, a focal length $f_0=320$ mm, a reference wavelength ${\lambda }_0=550$ nm, a wavelength range of 500–800 nm, and 7 annular zones. Through algorithmic recovery, we achieve clear imaging in the visible spectrum, with a peak signal-to-noise ratio (PSNR) of 22.85 dB, a correlation coefficient of 0.9596, and a root mean square error (RMSE) of 0.02, verifying the algorithm’s effectiveness.

Diffractive optical element (DOE)-based snapshot multispectral imaging provides a compact and lightweight solution for spectral acquisition. However, diffraction-induced degradations typically cause low contrast, structural blurring, and color cast, limiting both spatial and spectral imaging quality. Existing reconstruction methods still struggle to achieve satisfactory restoration quality under such degradations. Vision-language models (VLMs) can provide sharp and visually rich restoration priors, but their generated results may contain hallucinated textures or spurious details. Consequently, these priors cannot be directly injected into physical spectral reconstruction. To address this problem, we propose the Gated Flow Enhancer (GFE), a two-stage backend enhancement framework that conservatively uses VLM priors to improve reconstruction quality without modifying the DOE hardware or pretrained solvers. GFE first constructs an admitted guide image through a discrepancy-aware admission mechanism. It then enhances the initial multispectral estimate using gated flow updates within a frozen spectral latent space. This design selectively transfers beneficial structural information while suppressing content inconsistent with the real image. On multiple datasets and with different pretrained solvers, GFE consistently improves reconstruction quality, with average gains of 3.02 dB in PSNR and 0.098 in SSIM and a 2.66 reduction in SAM. To validate practical measurement performance, we further build a real DOE-based prototype and conduct real-world experiments using ColorChecker measurements and a 9-channel reference spectral camera. The results show that GFE improves both visual and spectral quality in real captures and yields more consistent spectral responses across independent measurement devices.

Diffractive optical elements (DOEs) provide an ultra-lightweight route to large-aperture imaging and are therefore attractive for lightweight large-aperture remote sensing. However, images directly captured by DOE-based diffraction systems often suffer from blur and low contrast caused by order aliasing, spatially varying blur, and uncertain noise levels. These degradations reduce image interpretability in remote sensing scenes, while the lack of large-scale paired diffraction datasets further limits data-driven restoration. To address this problem, we propose PLDIR, a diffraction remote sensing image restoration framework that integrates maximum a posteriori (MAP) estimation with a latent diffusion prior. PLDIR reduces manual noise-level tuning and alleviates the limitation of globally uniform denoising strength in conventional plug-and-play restoration. The framework contains three stages. Stage 1 trains a latent relationship encoder (LRE) to capture the latent relationship $H_{gt}$ between clean and noisy images. Stage 2 freezes the trained LRE and learns a latent diffusion denoising model (LDDM) to predict $H_{prev}$ from noisy images, providing a noise-aware prior for adaptive denoising. Stage 3 embeds the LDDM into the half-quadratic splitting (HQS) framework, where $H_{prev}$ regulates both the denoising strength and the spatially adaptive penalty matrix during iteration. Experiments on synthetic diffraction remote sensing data and real captured diffraction images demonstrate that PLDIR improves restoration quality and detail preservation over representative baselines, providing an effective restoration approach for lightweight diffraction remote sensing.