The paper introduces Context-Aware Mutual Learning (CAML) for blind image inpainting, a two-stage approach that jointly learns mask estimation and image inpainting. CAML employs two complementary learners—Inpainting-Guided Context-Mutual (IGCM) and Estimation-Guided Context-Mutual (EGCM)—to share contextual details and semantics between the two tasks. Experiments demonstrate that CAML outperforms existing methods on blind inpainting and related vision tasks such as snow, shadow, and watermark removal.
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.
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...
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...
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...
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.
arXiv:2610.28590v1 Announce Type: cross Abstract: Camera-trap imagery presents substantial variation in animal appearance, illumination, background clutter, and visual degradation. Masked autoencoder...
arXiv:2607.18227v2 Announce Type: replace Abstract: In line with the prevailing direction of vision research, we explore the integration of both generation and editing capabilities for video and imag...
arXiv:2609.40347v1 Announce Type: new Abstract: We introduce VideoMSN, a Masked Siamese Network framework for efficient self-supervised spatio-temporal representation learning in videos. Instead of r...
Removing an object from a real image requires more than synthesizing plausible content within a mask: the method must suppress residual object features, preserve the unedited scene, and generate repla...
arXiv:2606. 27584v1 Announce Type: cross Abstract: 3D scene inpainting is essential for reconstructing areas corrupted by occlusions or limited viewpoints.
arXiv:2608. 20212v1 Announce Type: new Abstract: High-fidelity removal of eyeglasses from video is a major challenge in facial attribute editing, as the underlying facial geometry is often obscured by complex refractive distortions and view-dependent specular reflections.