Advanced Image Processing Techniques

Últimos artículos

• FANVIDv2: Evaluating Video Super-Resolution by Face and Licence-Plate Recognition Under Compound Degradation

  Kavitha Viswanathan, Vrinda Goel, Shlesh Gholap, Devayan Ghosh, Madhav Gupta, Dhruvi Ganatra, Sanket Potdar, Amit Sethi · 1 de octubre de 2026

  Video super-resolution (VSR) is normally judged by PSNR and SSIM on clips that were downsampled bicubically, although in surveillance its purpose is to make faces and licence plates \emph{recognisable}. We present FANVIDv2, a benchmark that scores VSR by what a recognition pipeline can do with its o…

• PLSR: Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusion

  Yuxin Liu, Minshan Xie, Jiawen Liang, Runsong Zhu, Chi-Wing Fu, Tien-Tsin Wong · 28 de septiembre de 2026

  High-resolution 3D asset generation is vital in various 3D applications. Existing state-of-the-art diffusion-based models remain constrained by fixed resolutions, limiting their ability to produce details. In this paper, we tackle the challenge of generating more detailed, higher-resolution 3D objec…

• TaskIR: Task-Driven Image Restoration via Degradation Adaptation and Task Feedback

  Yanjie Tu, Qingsen Yan, Axi Niu, Wenxuan Cai, Tao Hu, Wei Dong, Haokui Zhang · 28 de septiembre de 2026

  Task-driven image restoration aims to improve both image quality and downstream task performance. However, existing methods predominantly focus on single degradation type and struggle to handle the diverse degradations encountered in real-world scenarios. Different degradations impose distinct resto…

• PhoenixSR: Generative Heterogeneous Distillation Unleashes Efficient Models for Real-World Super-Resolution

  Xin Di, Mingyu Shi, Yuanfei Bao, Long Peng, Yue Zhao, Jiaming Guo, Renjing Pei, Xueyang Fu, Yang Cao, Zheng-Jun Zha · 28 de septiembre de 2026

  Real-world image super-resolution (SR) requires recovering perceptually realistic high-resolution images from complex low-resolution observations while preserving faithful content. Diffusion-based SR benefits from strong generative priors but incurs substantial computational overhead, whereas feed-f…

• TOLA: Text-aware One-Step Latent Adaptation for Diffusion-based Text Image Super-Resolution

  Yike Xu, Yue Shi, Yong Guo, Jiezhang Cao · 25 de septiembre de 2026

  Text image super-resolution (TSR) aims to recover visually faithful and readable text under unknown degradations. Existing diffusion-based methods typically rely on multi-step prediction of either the high-resolution image or its text prior, resulting in prohibitive computational cost and inference …

• Super-Resolution of Solar Magnetograms via Adaptive Stratified Ensemble Learning with Uncertainty Estimation

  Sina Norouzi Kandalan, Haodi Jiang, Jason T. L. Wang, Qin Li · 24 de septiembre de 2026

  Single-image super-resolution of Sun's photospheric magnetograms enables consistent analysis across heterogeneous space-based instruments and supports long-term studies of solar magnetic field evolution. We address the super-resolution task from SOHO/MDI (low-resolution) to SDO/HMI (high-resolution)…

• TTTIR: Unlocking Instance-Specific State Evolution via Test-Time Training for Image Restoration

  Kaihang Zheng, Jun Li, Hang Guo, Hongyu Chi, Zimo Liu, Tao Dai, Jinpeng Wang, Yaowei Wang · 23 de septiembre de 2026

  Image restoration is inherently challenging due to the diverse and highly input-dependent nature of real-world degradations. While recent architectures like Transformers and state-space models have advanced the field, they predominantly rely on static, globally shared parameters, which struggle to f…

• Efficient Concertormer for Image Deblurring and Beyond

  Pin-Hung Kuo, Jinshan Pan, Shao-Yi Chien, Ming-Hsuan Yang · 22 de septiembre de 2026

  The Transformer architecture has achieved remarkable success in natural language processing and high-level vision tasks over the past few years. However, the inherent complexity of self-attention is quadratic to the size of the image, leading to unaffordable computational costs for high-resolution v…

• SkillIR: Evolving Scene-Aware Skills for Agentic Image Restoration

  Jie Shao, Shengkai Hu, Xu Zhang, Beihang Song, Yongcheng Jing, Xu Wu, Jun Wan · 21 de septiembre de 2026

  This paper studies agentic image restoration, in which multimodal agents coordinate specialized restoration tools to recover images affected by complex degradations. Existing restoration agents often derive complete tool-use plans from the original degraded image or retrieve previously successful tr…

• GraLoD: Graphics-Inspired Continuous Level-of-Detail Learning for Image Restoration

  Hu Gao, Lizhuang Ma, Yulong Chen · 16 de septiembre de 2026

  The spatial support required for image restoration varies across degradation types, image regions, and reconstruction stages. However, most existing methods rely on predefined multi-scale hierarchies and aggregate features through fixed fusion or attention, leaving the representation scale itself la…

• FreeTransformSR: Efficient Lightweight Image Super-Resolution via Free Low-Rank Learnable Transform

  Hongji Li, Yunhui Li · 11 de septiembre de 2026

  Single image super-resolution aims to reconstruct high-resolution images from low-resolution inputs. This paper proposes FreeTransformSR, a novel lightweight super-resolution network based on a channel-wise free low-rank learnable transform. The transform learns task-adaptive basis functions in a da…

• Guided Super-Resolution of Digital Elevation Models with Diffusion-Based Image Generators

  Armand Mihai Nicolicioiu, Dominik Narnhofer, Nando Metzger, Daniel Panangian, Ksenia Bittner, Konrad Schindler · 11 de septiembre de 2026

  High-resolution digital surface models (DSMs) play an important role in urban analysis, 3D building reconstruction, and infrastructure monitoring, yet their availability remains limited due to the high cost and complexity of data acquisition. In contrast, coarse DSMs from commercial satellite missio…

• OracleZoom: On-Policy Self-Distillation Inspired Reference-Constrained Recursive Image Super Resolution

  Shubhashis Roy Dipta, Sourajit Saha, Shaswati Saha, Nobin Sarwar · 9 de septiembre de 2026

  Recursive Super-Resolution (SR) extends fixed-scale SR to extreme magnification by repeatedly feeding predictions back into the same model, analogous to zooming an image repeatedly. However, ground truth availability at every scale, especially at depth, remains challenging as the required source res…

• SPARK: Input-Conditioned Sparse Activation Modulation for Frozen DiT-based Super-Resolution

  Federico Putamorsi, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi · 4 de septiembre de 2026

  Real-world image super-resolution (SR) increasingly relies on Diffusion Transformer (DiT) backbones, whose internal activations can be dominated by a small number of massive channels. Yet improving perceptual quality in these models still typically requires fine-tuning the network or attaching addit…

• Advanced Pixel Diffusion Model with Guided Sparse Global Refinement

  Weiyi You, Jinhua Zhang, Xingyu Zhou, Wei Long, Junyu Lou, Shuhang Gu · 2 de septiembre de 2026

  Pixel-space diffusion has recently emerged as a promising direction for high-fidelity image generation by modeling images directly in the original pixel domain. However, pixel-space diffusion is computationally demanding due to the extremely high dimensionality of natural images. For efficiency, exi…

• Uncertainty-Guided Latent Diffusion Models for Faithful Super Resolution

  Ren Wang, Yung-Yu Chuang · 27 de agosto de 2026

  The perception-distortion trade-off poses a fundamental challenge in single-image super-resolution (SR). Although diffusion-based SR methods excel at generating perceptually realistic images, achieving high fidelity remains a key limitation. Recent advances in diffusion-based SR have shown promise i…

• GraftSR: Grafting Authentic Textures for Real-World Image Super-Resolution via Identical-Instance Guidance

  Qifan Yu, Haoran Bai, Zongyao He, Weijie He, Sibin Deng, Honggang Qi, Ying Chen · 27 de agosto de 2026

  Diffusion-based real-world image super-resolution (SR) achieves impressive perceptual quality but inherently suffers from severe texture hallucination. To overcome this limitation, we propose GraftSR, a texture-reference-guided generative SR framework that leverages reference images of the identical…

• WAVE: Reversing the Guidance Hierarchy for Coarse-to-Fine Guided Depth Super-Resolution

  Tayyab Nasir, Daochang Liu, Ajmal Mian · 27 de agosto de 2026

  Guided depth super-resolution (GDSR) typically extracts RGB guidance features through convolutional hierarchies, inheriting their fine-to-coarse bias. Thus, low-level spatial cues surface in early layers, leaving the deeper layers to suppress those that do not correspond to true depth boundaries, wh…

• Controllable blind deblurring with diffusion models

  Imane Si Salah, Emile Cribelier, Thomas Veit, Wolf Hauser, Arthur Leclaire · 26 de agosto de 2026

  Image acquisition with a camera involves several degradations due to the optical system, sensor, or low-level processing steps. We address blind deblurring in professional photography: we aim to invert unknown isotropic blur without knowledge of the degradation kernel. For such inverse problems,wher…

• GAN-Diff : Coupling Pretrained WGAN-GP Features with Conditional Diffusion U-Nets

  Saif Ahmed, Ashadulla Hil Galib, S. M. Riaz Rahman Antu, Ahmed Faizul Haque Dhrubo, Souvik Pramanik, Mohammad Abdul Qayum, Mohsin Sajjad, Mohammad Ashrafuzzaman Khan · 25 de agosto de 2026

  Generative adversarial networks (GANs) can provide efficient image generation, while diffusion models offer high-quality image restoration but require iterative sampling. This paper presents a hybrid GAN-guided diffusion framework that uses a pretrained Wasserstein GAN with gradient penalty (WGAN-GP…

• Ultra-High-Definition Restoration Transformers with Correlation Matching Transformation

  Cong Wang, Liyan Wang, Jinshan Pan, Wei Wang, Wenqi Ren, Jun Liu, Xiaochun Cao · 21 de agosto de 2026

  We propose UHDformer++, a general Transformer-based framework to solve numerous Ultra-High-Definition (UHD) image restoration tasks. UHDformer++ operates across $4$ coordinated learning spaces: 1) a high-resolution space (HR) for multi-level feature extraction, 2) a low-resolution space (LR) for lea…

• Enhancing EBSD throughput of battery electrode materials using super-resolution generative adversarial networks

  John Mangum, Andrew Glaws, Francois Usseglio-Viretta, Steven Spurgeon, Donal Finegan · 20 de agosto de 2026

  Quantitative microstructural characterization of Li-ion battery electrode materials using electron backscatter diffraction (EBSD) has been proven as a critical method for optimizing cell performance. However, the inherently slow nature of EBSD can hinder the throughput of analyses needed for statist…

• EDITBRIDGE: Towards Faithful and Efficient Ultra-High-Resolution Image Editing

  Jiayi Song, Shijie Huang, Fangtai Wu, Yubo Huang, Zhenxiong Tan, Songhua Liu, Jiaming Liu, Ruihua Huang · 19 de agosto de 2026

  High-resolution image editing is increasingly demanded in professional workflows, yet existing diffusion-based models remain constrained to resolutions below 1K due to quadratic attention complexity and prohibitive memory requirements. A prevalent workaround employs a two-stage pipeline: editing at …

• SFMformer: A Spatial-Frequency Modulation Transformer for Lightweight Image Super-Resolution

  Chih-Hsiang Yang, Chia-Min Lin, Ching-Yu Tsai, Yung-Che Wang, Jen-Shiun Chiang · 19 de agosto de 2026

  Sparse attention mechanisms, which score all token pairs but propagate only the strongest, now underpin the most efficient Transformers for lightweight image super-resolution. This paper observes that sparsification changes what it means to improve such a network. A dense attention layer has one pla…

• Improving Complex Moiré Removal with Generative Supervision

  Xinyang Gu, Zhilu Zhang, Honglei Xu, Yanting Mei, Yukang Ding, Wangmeng Zuo · 19 de agosto de 2026

  The availability of high-quality paired data is essential for training learning-based image demoiréing models. However, it remains challenging for existing datasets to encompass the complex moiré patterns captured in uncontrolled real-world scenarios. Such degradations typically manifest as large-sc…