image color processing

This work addresses the insufficient accuracy of the Oklab/Oklch color spaces in predicting perceptual color differences, which limits their utility in high-consistency design and interpolation tasks. The authors propose Oklch+, an enhanced color space incorporating a power transformation along the lightness (L) axis and Naka–Rushton–based saturation compression along the chroma (C) axis, yielding a model with only three tunable parameters. Perceptual color differences are approximated using Euclidean distance in this modified space. Optimized on the COMBVD dataset, Oklch+ achieves a STRESS value of 29.09—comparable to CIEDE2000 (29.13)—and further improves to 26.14 on the BFD-P D65 holdout set, substantially outperforming original Oklab (51.45). Thus, Oklch+ approaches the predictive performance of CIEDE2000 while retaining simplicity and interpolability.

This paper addresses three critical challenges in high-resolution image processing: grayscale quantization distortion, low feature extraction accuracy, and non-invertible transformations. To this end, we propose a modular spatial image processing framework. Methodologically, it employs a multi-level collaborative algorithm architecture integrating structure-preserving 8-level grayscale discretization, RGB/YCrCb histogram equalization, HSV brightness adaptive adjustment, 3×3 convolution-based sharpening and unsharp masking, gamma correction, and joint extraction of Canny edges, Hough lines, Harris corners, and morphological geometric features. Additionally, a reversible bidirectional transformation pipeline is designed to ensure consistency between forward processing and inverse reconstruction. Experimental results demonstrate a bidirectional transformation fidelity of 76.10% (forward) and 74.80% (inverse), cue stick angle estimation error below 0.5° (measured at 51.50°), cue isolation similarity to the original image of 81.87%, and strong robustness across multiple benchmark datasets.

This work addresses the limitations of conventional image colorization methods, which operate in Lab space with a fixed luminance channel and thus struggle with non-standard grayscale inputs—such as orthochromatic photographs—and cannot adjust brightness. To overcome this, the authors propose a luminance-agnostic colorization framework that treats colorization as a full RGB image editing task. They introduce a hybrid grayscale training objective that uniformly accommodates both modern panchromatic and historical orthochromatic photographic inputs. By removing the fixed-luminance constraint, the method significantly enhances robustness to unnatural grayscale images. Experiments demonstrate superior performance on COCO, ImageNet, and multi-instance benchmarks, with markedly reduced color artifacts on orthochromatic images. Human evaluations confirm that the visual quality of the results substantially surpasses that of existing approaches.

To address systematic color bias and high-frequency texture blurring in the DDColor model—caused by insufficient frequency-domain modeling and constrained input dimensionality—this paper proposes a dual-reconstruction framework jointly optimizing color fidelity and frequency representation. Methodologically, we introduce a lightweight color correction module and a learnable frequency compensation mechanism to collaboratively enhance chromatic accuracy and high-frequency detail recovery. Furthermore, we integrate RGB-YUV space mapping, frequency-domain feature disentanglement, and backbone network fine-tuning to enable multi-scale feature co-optimization. Experimental results demonstrate that our approach significantly outperforms the original DDColor model in both PSNR and SSIM metrics. Qualitatively, colorimetric fidelity and structural sharpness are substantially improved, effectively mitigating the model’s inherent chromatic biases and texture degradation.

Existing image recoloring and editing methods struggle to simultaneously achieve precise local color control, faithful texture preservation, and consistent color reproduction across luminance-varying regions. To address this, we propose a diffusion-based, quantized palette-driven editing framework. Our method uniquely employs quantized images as direct inputs to the diffusion model, enhancing interpretability and controllability. We design a weighted bipartite graph matching algorithm to enable semantically coherent, extreme palette transfer. Furthermore, we introduce multi-scale texture conditioning—optimized via thresholded gradient guidance—and JPEG-noise-robust training, overcoming the brightness-invariance limitation inherent in prior approaches. Extensive experiments demonstrate state-of-the-art performance: high-fidelity reconstruction, strict adherence to target palettes, and superior texture consistency. The framework supports both localized recoloring with fine-grained control and end-to-end palette migration, establishing a new benchmark for controllable, photorealistic image editing.

研究开发了一种开源颜色分级工具，并使用K-最近邻算法来预测色调区域阈值，以实现更有效的图像增强。

This work addresses two types of chromatic artifacts—hue looping and neutral-axis deviation—that arise during OKLCH interpolation in low-saturation regions, noting that existing approaches only mitigate the latter. To resolve both issues simultaneously, the authors propose a continuously differentiable chroma-gated interpolation mechanism that smoothly blends between OKLCH and linear Oklab paths. Built upon the Oklab color space, the method employs a single-parameter gating function of Michaelis–Menten form, $w(C) = C^n / (C^n + \sigma^n)$, enabling differentiable fusion without requiring thresholds or endpoint detection. Experimental results demonstrate that, under default parameters, the approach reduces the average lateral deviation of hue trajectories by 49.5% and decreases chroma-weighted hue shift by 35.5%, offering a generic and backward-compatible solution for modern CSS color interpolation.

This work addresses the inherent ambiguity in grayscale image colorization, which often leads to semantically inaccurate color predictions. To mitigate this issue, the authors propose incorporating CLIP-based textual conditioning as a guidance signal and present the first systematic evaluation—under controlled conditions—of how text prompts influence colorization performance across two distinct architectures: a U-Net and Stable Diffusion 1.5. Experimental results demonstrate that text guidance substantially enhances colorization quality: for the U-Net, PSNR improves by 5.6%, SSIM by 1.2%, colorfulness by 36.6%, and LPIPS decreases by 7.6%. Consistent improvements are also observed with Stable Diffusion, confirming the effectiveness and generalizability of text-guided colorization across different model architectures.

This work addresses color correction inaccuracies in digital cameras under complex spectral and high-chromaticity LED illumination, which arise from the nonlinear relationship between sensor responses and the CIE XYZ color space. To mitigate this, the authors propose an illumination-adaptive 3D lookup table framework, termed C²LUT, that integrates chromaticity-aware illumination representation with nonlinear color transformation. The method employs Tucker tensor decomposition to compress the lookup table, achieving a favorable trade-off between colorimetric accuracy and hardware deployment efficiency. Evaluated on a large-scale dataset comprising 1,473 spectral illuminants, C²LUT demonstrates consistent performance gains across multiple cameras, diverse lighting conditions, and real-world imagery—reducing CIE ΔE₀₀ error by up to 20% and angular error by as much as 18%, while adhering to the computational constraints of modern image signal processors (ISPs).

This study addresses the limitations of existing colormap tools, which struggle to simultaneously support high-frequency luminance variation, rich color representation, and aesthetic harmony—constraints that impede feature identification in complex data visualizations. To overcome this, the authors propose an interactive colormap extraction method that, for the first time, automatically constructs colormaps from artworks and natural images, achieving both high luminance dynamic range and strong aesthetic quality. By integrating image analysis, color design principles, and luminance modeling, the approach transcends the traditional trade-off between visual appeal and functional effectiveness in colormap design. Experimental results demonstrate that the generated colormaps significantly enhance users’ efficiency in identifying structural features and contour lines, as well as their accuracy in interpreting data during exploratory visualization tasks.