Multi-modality image fusion (MMIF) enhances scene representation by exploiting complementary cues from different modalities. Adverse weather, however, causes significant image degradation, disrupting feature representation and requiring simultaneous feature restoration and cross-modal complementarity.
FreeInpaint is a feed‑forward 3D inpainting framework that reconstructs complete, geometrically consistent scenes directly from unposed multi‑view images with masked regions. It extends a 3D foundation model by propagating masked areas across views, using a Learnable Mask Attention mechanism to maintain reliable cross‑view correspondences and a Support Token Refinement strategy that injects diffusion‑generated auxiliary tokens for high‑fidelity completion. Experiments on diverse datasets show that FreeInpaint delivers superior inpainting quality without requiring pre‑computed camera poses, while maintaining fast inference speed.
By Jingyi Pan, Dan Xu, Qiong Luo
arXiv:2609.14377v1 Announce Type: new
Abstract: Camouflage image generation (CIG) aims to synthesize realistic camouflaged images by blending foreground objects into concealment-compatible background...
By Haodong Yang, Zhongling Huang, Gong Cheng
arXiv:2603.23463v2 Announce Type: replace-cross
Abstract: Recent diffusion-based models achieve photorealism in image inpainting but require many sampling steps, limiting practical use. Few-step text...
By Duc Vu, Kien Nguyen, Trong-Tung Nguyen, Ngan Nguyen, Phong Nguyen, Khoi Nguyen, Cuong Pham, Anh Tran
arXiv:2610.07916v1 Announce Type: new
Abstract: Image Manipulation Localization (IML) is commonly formulated as a fully supervised learning task that estimates the optimal manipulation mask $y$ for a...
By Xuekang Zhu, Kaiwen Feng, Ruifeng Wang, Xiwen Wang, Xiaochen Ma, Bo Du, Changjiang Jiang, Chenfan Qu, Songyu Ye, Xia Du, Wentao Feng, Jian Liu, Ji-Zhe Zhou
The paper introduces PRIN, a Perceptual-Structural Retinex-Inspired Network designed for low‑light image enhancement. PRIN uses a shared encoder with asymmetric decoders to separately refine reflectance and illumination components, applying attention‑guided wavelet refinement for textures and edge preservation in the reflectance branch and spatially coherent brightness adjustment in the illumination branch. A curve‑based post‑composition head further corrects tone and exposure, achieving state‑of‑the‑art SSIM, LPIPS, and PSNR results on multiple LOL datasets.
By Qiyi Xie, Wei Chen Ng, Ziyao Li, Yuning Wu, Huasen Wu, Tianshi Miao, Jian Ding Tan