{"total":33,"rows":[{"id":"2205.07514","title":"Residual Local Feature Network for Efficient Super-Resolution","created":"2022-05-16","cited_by_count":53,"area":null},{"id":"2306.11920","title":"NILUT: Conditional Neural Implicit 3D Lookup Tables for Image Enhancement","created":"2023-06-20","cited_by_count":31,"area":null},{"id":"2105.10288","title":"Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices","created":"2021-05-21","cited_by_count":30,"area":null},{"id":"2207.10345","title":"CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution","created":"2022-07-21","cited_by_count":27,"area":null},{"id":"2207.10392","title":"FADE: Fusing the Assets of Decoder and Encoder for Task-Agnostic Upsampling","created":"2022-07-21","cited_by_count":25,"area":null},{"id":"2105.09750","title":"Anchor-based Plain Net for Mobile Image Super-Resolution","created":"2021-05-20","cited_by_count":24,"area":null},{"id":"2402.03412","title":"See More Details: Efficient Image Super-Resolution by Experts Mining","created":"2024-02-05","cited_by_count":23,"area":null},{"id":"2207.12577","title":"Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution","created":"2022-07-25","cited_by_count":20,"area":null},{"id":"2304.07018","title":"DIPNet: Efficiency Distillation and Iterative Pruning for Image Super-Resolution","created":"2023-04-14","cited_by_count":19,"area":null},{"id":"2304.13385","title":"Low-field magnetic resonance image enhancement via stochastic image quality transfer","created":"2023-04-26","cited_by_count":19,"area":null},{"id":"2404.16484","title":"Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey","created":"2024-04-25","cited_by_count":19,"area":null},{"id":"2211.05910","title":"Efficient and Accurate Quantized Image Super-Resolution on Mobile NPUs, Mobile AI & AIM 2022 challenge: Report","created":"2022-11-07","cited_by_count":16,"area":null},{"id":"2211.03885","title":"Learned Smartphone ISP on Mobile GPUs with Deep Learning, Mobile AI & AIM 2022 Challenge: Report","created":"2022-11-07","cited_by_count":13,"area":null},{"id":"2211.06770","title":"MicroISP: Processing 32MP Photos on Mobile Devices with Deep Learning","created":"2022-11-08","cited_by_count":13,"area":null},{"id":"2108.10335","title":"edge-SR: Super-Resolution For The Masses","created":"2021-08-23","cited_by_count":9,"area":null},{"id":"2210.06965","title":"CUF: Continuous Upsampling Filters","created":"2022-10-13","cited_by_count":8,"area":null},{"id":"2305.03387","title":"AsConvSR: Fast and Lightweight Super-Resolution Network with Assembled Convolutions","created":"2023-05-05","cited_by_count":8,"area":null},{"id":"2211.06263","title":"PyNet-V2 Mobile: Efficient On-Device Photo Processing With Neural Networks","created":"2022-11-08","cited_by_count":7,"area":null},{"id":"2208.11609","title":"Fast Nearest Convolution for Real-Time Efficient Image Super-Resolution","created":"2022-08-24","cited_by_count":6,"area":null},{"id":"2305.02126","title":"Bicubic++: Slim, Slimmer, Slimmest -- Designing an Industry-Grade Super-Resolution Network","created":"2023-05-03","cited_by_count":6,"area":null},{"id":"2307.13337","title":"Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks","created":"2023-07-25","cited_by_count":5,"area":null},{"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,"area":null},{"id":"2208.14839","title":"QuantNAS for super resolution: searching for efficient quantization-friendly architectures against quantization noise","created":"2022-08-31","cited_by_count":4,"area":null},{"id":"2211.04470","title":"Efficient Single-Image Depth Estimation on Mobile Devices, Mobile AI & AIM 2022 Challenge: Report","created":"2022-11-07","cited_by_count":4,"area":null},{"id":"2208.11608","title":"Sliding Window Recurrent Network for Efficient Video Super-Resolution","created":"2022-08-24","cited_by_count":3,"area":null},{"id":"2208.14655","title":"XCAT -- Lightweight Quantized Single Image Super-Resolution using Heterogeneous Group Convolutions and Cross Concatenation","created":"2022-08-31","cited_by_count":3,"area":null},{"id":"2211.05256","title":"Power Efficient Video Super-Resolution on Mobile NPUs with Deep Learning, Mobile AI & AIM 2022 challenge: Report","created":"2022-11-07","cited_by_count":3,"area":null},{"id":"2407.13500","title":"FADE: A Task-Agnostic Upsampling Operator for Encoder-Decoder Architectures","created":"2024-07-18","cited_by_count":3,"area":null},{"id":"2206.01777","title":"Real-Time Super-Resolution for Real-World Images on Mobile Devices","created":"2022-06-03","cited_by_count":2,"area":null},{"id":"2211.06769","title":"Realistic Bokeh Effect Rendering on Mobile GPUs, Mobile AI & AIM 2022 challenge: Report","created":"2022-11-07","cited_by_count":2,"area":null},{"id":"2212.09501","title":"NAWQ-SR: A Hybrid-Precision NPU Engine for Efficient On-Device Super-Resolution","created":"2022-12-15","cited_by_count":1,"area":null},{"id":"2308.11365","title":"Towards Clip-Free Quantized Super-Resolution Networks: How to Tame Representative Images","created":"2023-08-22","cited_by_count":0,"area":null},{"id":"2501.01773","title":"Compressed Domain Prior-Guided Video Super-Resolution for Cloud Gaming Content","created":"2025-01-03","cited_by_count":0,"area":null}],"next_cursor":null}