Purple fringing, a persistent artifact caused by Longitudinal Chromatic Aberration (LCA) in camera lenses, has long degraded the clarity and realism of digital imaging. Traditional solutions rely on complex and expensive apochromatic (APO) lens hardware and the extraction of handcrafted features, ignoring the data-driven approach. To fill this gap, we introduce DCA-LUT, the first deep learning framework for purple fringing removal. Inspired by the physical root of the problem-the spatial misalignment of RGB color channels due to lens dispersion, we introduce a novel Chromatic-Aware Coordinate Transformation (CA-CT) module, learning an image-adaptive color space to decouple and isolate fringing into a dedicated dimension. This targeted separation allows the network to learn a precise "purple fringe channel," which then guides the accurate restoration of the luminance channel. The final color correction is performed by a learned 5D Look-Up Table (5D LUT), enabling efficient and powerful non-linear color mapping. To enable robust training and fair evaluation, we constructed a large-scale synthetic purple fringing dataset (PF-Synth). Extensive experiments in synthetic and real-world datasets demonstrate that our method achieves state-of-the-art performance in purple fringing removal.

Purple flare, a diffuse chromatic aberration artifact commonly found around highlight areas, severely degrades the tone transition and color of the image. Existing traditional methods are based on hand-crafted features, which lack flexibility and rely entirely on fixed priors, while the scarcity of paired training data critically hampers deep learning. To address this issue, we propose a novel network built upon decoupled HSV Look-Up Tables (LUTs). The method aims to simplify color correction by adjusting the Hue (H), Saturation (S), and Value (V) components independently. This approach resolves the inherent color coupling problems in traditional methods. Our model adopts a two-stage architecture: First, a Chroma-Aware Spectral Tokenizer (CAST) converts the input image from RGB space to HSV space and independently encodes the Hue (H) and Value (V) channels into a set of semantic tokens describing the Purple flare status; second, the HSV-LUT module takes these tokens as input and dynamically generates independent correction curves (1D-LUTs) for the three channels H, S, and V. To effectively train and validate our model, we built the first large-scale purple flare dataset with diverse scenes. We also proposed new metrics and a loss function specifically designed for this task. Extensive experiments demonstrate that our model not only significantly outperforms existing methods in visual effects but also achieves state-of-the-art performance on all quantitative metrics.

Ultra-high-definition (UHD) image restoration is vital for applications demanding exceptional visual fidelity, yet existing methods often face a trade-off between restoration quality and efficiency, limiting their practical deployment. In this paper, we propose TSFormer, an all-in-one framework that integrates Trusted learning with Sparsification to boost both generalization capability and computational efficiency in UHD image restoration. The key to sparsification is that only a small amount of token movement is allowed within the model. To efficiently filter tokens, we use Min- $p$ with random matrix theory to quantify the uncertainty of tokens (lower trustworthiness), thereby improving the robustness of the model. Our model can run a 4K ( $3840\times 2160$ ) image in real time (40fps) with 3.38 M parameters. Extensive experiments demonstrate that TSFormer achieves state-of-the-art restoration quality while enhancing generalization and reducing computational demands. In addition, our token filtering method can be applied to other image restoration models to effectively accelerate inference and maintain performance.

Captured polyp images often suffer from degradation, such as dim lighting, blur, and overexposure. Direct segmentation is prone to artifact diffusion, which significantly degrades the performance of downstream segmentation algorithms and leads to inaccurate boundary delineation. Addressing these varied degradations requires a dynamic, intelligent process that diagnoses and applies targeted corrections. We present AgentPolyp, a novel framework driven by an intelligent agent that integrates CLIP-based semantic guidance and dynamic image enhancement with a lightweight segmentation network. The agent adaptively selects reinforcement learning strategies to perform context-aware denoising, contrast adjustment, and artifact reduction. This selection process is continuously optimized through a feedback loop that includes quality assessment, ensuring the optimization and enhancement of downstream segmentation. This approach addresses degradation complexity and feature compatibility issues, offering a deployable solution for endoscopic polyp analysis.