{"list":"other","total":55,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1801.04381","title":"MobileNetV2: Inverted Residuals and Linear Bottlenecks","created":"2018-01-13","cited_by_count":4795},{"id":"1709.01507","title":"Squeeze-and-Excitation Networks","created":"2017-09-05","cited_by_count":4789},{"id":"2111.09881","title":"Restormer: Efficient Transformer for High-Resolution Image Restoration","created":"2021-11-18","cited_by_count":1627},{"id":"1807.02758","title":"Image Super-Resolution Using Very Deep Residual Channel Attention Networks","created":"2018-07-08","cited_by_count":1264},{"id":"1609.05158","title":"Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network","created":"2016-09-16","cited_by_count":1204},{"id":"1707.01083","title":"ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices","created":"2017-07-04","cited_by_count":1200},{"id":"2102.02808","title":"Multi-Stage Progressive Image Restoration","created":"2021-02-04","cited_by_count":816},{"id":"2204.04676","title":"Simple Baselines for Image Restoration","created":"2022-04-10","cited_by_count":708},{"id":"1905.02716","title":"EDVR: Video Restoration with Enhanced Deformable Convolutional Networks","created":"2019-05-07","cited_by_count":494},{"id":"2003.10428","title":"Deep Unfolding Network for Image Super-Resolution","created":"2020-03-23","cited_by_count":254},{"id":"1811.11127","title":"Unprocessing Images for Learned Raw Denoising","created":"2018-11-27","cited_by_count":250},{"id":"1908.02197","title":"Neural Blind Deconvolution Using Deep Priors","created":"2019-08-06","cited_by_count":113},{"id":"2002.05509","title":"Replacing Mobile Camera ISP with a Single Deep Learning Model","created":"2020-02-13","cited_by_count":108},{"id":"2103.15061","title":"Invertible Image Signal Processing","created":"2021-03-28","cited_by_count":94},{"id":"2411.15269","title":"MambaIRv2: Attentive State Space Restoration","created":"2024-11-22","cited_by_count":94},{"id":"2303.16900","title":"InceptionNeXt: When Inception Meets ConvNeXt","created":"2023-03-29","cited_by_count":86},{"id":"2111.11745","title":"Intriguing Findings of Frequency Selection for Image Deblurring","created":"2021-11-23","cited_by_count":81},{"id":"2010.06935","title":"Practical Deep Raw Image Denoising on Mobile Devices","created":"2020-10-14","cited_by_count":76},{"id":"2204.08714","title":"NAFSSR: Stereo Image Super-Resolution Using NAFNet","created":"2022-04-19","cited_by_count":76},{"id":"2208.11247","title":"SwinFIR: Revisiting the SwinIR with Fast Fourier Convolution and Improved Training for Image Super-Resolution","created":"2022-08-24","cited_by_count":68},{"id":"2309.05239","title":"HAT: Hybrid Attention Transformer for Image Restoration","created":"2023-09-11","cited_by_count":66},{"id":"2310.11881","title":"A Comparative Study of Image Restoration Networks for General Backbone Network Design","created":"2023-10-18","cited_by_count":57},{"id":"2404.10343","title":"The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report","created":"2024-04-16","cited_by_count":57},{"id":"2403.19967","title":"Rewrite the Stars","created":"2024-03-29","cited_by_count":55},{"id":"2404.01547","title":"Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining","created":"2024-04-02","cited_by_count":54},{"id":"1905.12156","title":"Towards Real Scene Super-Resolution with Raw Images","created":"2019-05-29","cited_by_count":44},{"id":"2404.09790","title":"NTIRE 2024 Challenge on Image Super-Resolution (x4): Methods and Results","created":"2024-04-15","cited_by_count":40},{"id":"2105.07809","title":"Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report","created":"2021-05-17","cited_by_count":34},{"id":"1904.12945","title":"Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation","created":"2019-04-29","cited_by_count":30},{"id":"2404.11313","title":"NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results","created":"2024-04-17","cited_by_count":29},{"id":"2006.12030","title":"DO-Conv: Depthwise Over-parameterized Convolutional Layer","created":"2020-06-22","cited_by_count":28},{"id":"2504.10686","title":"The Tenth NTIRE 2025 Efficient Super-Resolution Challenge Report","created":"2025-04-14","cited_by_count":26},{"id":"2504.10685","title":"NTIRE 2025 Challenge on Cross-Domain Few-Shot Object Detection: Methods and Results","created":"2025-04-14","cited_by_count":25},{"id":"2504.12711","title":"NTIRE 2025 Challenge on Day and Night Raindrop Removal for Dual-Focused Images: Methods and Results","created":"2025-04-17","cited_by_count":22},{"id":"2504.12276","title":"The Tenth NTIRE 2025 Image Denoising Challenge Report","created":"2025-04-16","cited_by_count":21},{"id":"2404.17433","title":"PromptCIR: Blind Compressed Image Restoration with Prompt Learning","created":"2024-04-26","cited_by_count":20},{"id":"2210.13552","title":"Perceptual Image Enhancement for Smartphone Real-Time Applications","created":"2022-10-24","cited_by_count":18},{"id":"2106.13883","title":"Semi-Supervised Raw-to-Raw Mapping","created":"2021-06-25","cited_by_count":16},{"id":"2504.12401","title":"NTIRE 2025 Challenge on Event-Based Image Deblurring: Methods and Results","created":"2025-04-16","cited_by_count":16},{"id":"2504.13131","title":"NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results","created":"2025-04-17","cited_by_count":14},{"id":"2505.12089","title":"NTIRE 2025 Challenge on Efficient Burst HDR and Restoration: Datasets, Methods, and Results","created":"2025-05-17","cited_by_count":14},{"id":"2102.01579","title":"Exploiting Raw Images for Real-Scene Super-Resolution","created":"2021-02-02","cited_by_count":12},{"id":"2504.14582","title":"NTIRE 2025 Challenge on Image Super-Resolution (x4): Methods and Results","created":"2025-04-20","cited_by_count":12},{"id":"2505.03007","title":"NTIRE 2025 Challenge on UGC Video Enhancement: Methods and Results","created":"2025-05-05","cited_by_count":12},{"id":"2312.15487","title":"BSRAW: Improving Blind RAW Image Super-Resolution","created":"2023-12-24","cited_by_count":11},{"id":"2504.15003","title":"NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: KwaiSR Dataset and Study","created":"2025-04-21","cited_by_count":11},{"id":"2505.18988","title":"NTIRE 2025 Challenge on Video Quality Enhancement for Video Conferencing: Datasets, Methods and Results","created":"2025-05-25","cited_by_count":9},{"id":"1707.08350","title":"Modelling the Scene Dependent Imaging in Cameras with a Deep Neural Network","created":"2017-07-26","cited_by_count":8},{"id":"2505.16314","title":"NTIRE 2025 challenge on Text to Image Generation Model Quality Assessment","created":"2025-05-22","cited_by_count":8},{"id":"2409.18204","title":"Toward Efficient Deep Blind RAW Image Restoration","created":"2024-09-26","cited_by_count":7},{"id":"2404.16223","title":"Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey","created":"2024-04-24","cited_by_count":6},{"id":"2308.08137","title":"SYENet: A Simple Yet Effective Network for Multiple Low-Level Vision Tasks with Real-time Performance on Mobile Device","created":"2023-08-16","cited_by_count":5},{"id":"2308.12494","title":"MOFA: A Model Simplification Roadmap for Image Restoration on Mobile Devices","created":"2023-08-24","cited_by_count":5},{"id":"2405.05497","title":"Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution","created":"2024-05-09","cited_by_count":2}],"next_cursor":null}