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A Non-Linear Convolution Network for Image Processing

https://doi.org/10.3390/electronics10020201

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-17227-5_28openalex-snapshot

Faster Convolutional Neural Networks in Low Density FPGAs Using Block Pruning

inferredcites →https://doi.org/10.1007/978-3-030-58542-6_15openalex-snapshot

Fully Trainable and Interpretable Non-local Sparse Models for Image Restoration

inferredcites →https://doi.org/10.1007/978-3-319-46493-0_34openalex-snapshot

Learning Recursive Filters for Low-Level Vision via a Hybrid Neural Network

inferredcites →https://doi.org/10.1007/s10489-020-01845-7openalex-snapshot

Hybrid nonlinear convolution filters for image recognition

inferredcites →https://doi.org/10.1016/b978-012500451-0/50000-xopenalex-snapshot

Preface

inferredcites →https://doi.org/10.1016/j.micpro.2018.03.007openalex-snapshot

CaFPGA: An automatic generation model for CNN accelerator

inferredcites →https://doi.org/10.1088/1748-0221/13/07/p07027openalex-snapshot

Fast inference of deep neural networks in FPGAs for particle physics

inferredcites →https://doi.org/10.1109/cvpr.2016.181openalex-snapshot

Deeply-Recursive Convolutional Network for Image Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr.2016.207openalex-snapshot

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

inferredcites →https://doi.org/10.1109/cvpr.2016.90openalex-snapshot

Deep Residual Learning for Image Recognition

inferredcites →https://doi.org/10.1109/cvpr.2017.325openalex-snapshot

Kernel Pooling for Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00094openalex-snapshot

Modulated Convolutional Networks

inferredcites →https://doi.org/10.1109/cvpr.2018.00262openalex-snapshot

Residual Dense Network for Image Super-Resolution

inferredcites →https://doi.org/10.1109/cvpr.2019.00012openalex-snapshot

Kervolutional Neural Networks

inferredcites →https://doi.org/10.1109/iccv.2017.510openalex-snapshot

Non-linear Convolution Filters for CNN-Based Learning

inferredcites →https://doi.org/10.1109/iccv.2017.89openalex-snapshot

Deformable Convolutional Networks

inferredcites →https://doi.org/10.1109/iccv.2019.00426openalex-snapshot

Fully Convolutional Pixel Adaptive Image Denoiser

inferredcites →https://doi.org/10.1109/mdat.2019.2908549openalex-snapshot

Fune: An FPGA Tuning Framework for CNN Acceleration

inferredcites →https://doi.org/10.1109/reconfig.2018.8641716openalex-snapshot

Throughput-Optimized Frequency Domain CNN with Fixed-Point Quantization on FPGA

inferredcites →https://doi.org/10.1109/tcad.2017.2705069openalex-snapshot

Angel-Eye: A Complete Design Flow for Mapping CNN Onto Embedded FPGA

inferredcites →https://doi.org/10.1109/tip.2017.2662206openalex-snapshot

Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising

inferredcites →https://doi.org/10.1109/tip.2018.2839891openalex-snapshot

FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising

inferredcites →https://doi.org/10.1109/tnnls.2018.2808319openalex-snapshot

Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks

inferredcites →https://doi.org/10.1109/tpami.2018.2890623openalex-snapshot

Joint Image Filtering with Deep Convolutional Networks

inferredcites →https://doi.org/10.1109/tpami.2018.2890637openalex-snapshot

Perspective-Adaptive Convolutions for Scene Parsing

inferredcites →https://doi.org/10.1145/3061639.3062244openalex-snapshot

Exploring Heterogeneous Algorithms for Accelerating Deep Convolutional Neural Networks on FPGAs

inferredcites →https://doi.org/10.1145/3284357openalex-snapshot

NEURA ghe

inferredcites →https://doi.org/10.3390/electronics8030295openalex-snapshot

Optimized Compression for Implementing Convolutional Neural Networks on FPGA

inferredcites →https://doi.org/10.3390/electronics9111823openalex-snapshot

mNet2FPGA: A Design Flow for Mapping a Fixed-Point CNN to Zynq SoC FPGA

inferredcites →https://doi.org/10.48550/arxiv.1710.09282openalex-snapshot

A Survey of Model Compression and Acceleration for Deep Neural Networks

inferredcites →https://doi.org/10.48550/arxiv.1712.08934openalex-snapshot

A Survey of FPGA-Based Neural Network Accelerator

inferredcites →https://doi.org/10.5281/zenodo.5680908openalex-snapshot

fastmachinelearning/hls4ml: coris

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

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Frequency-domain multi-scale hybrid attention for pathological image classification

inferred← citeshttps://doi.org/10.1049/ipr2.12692openalex-snapshot

Nonlinear kernel based feature maps for blur‐sensitive unsharp masking of JPEG images

inferred← citeshttps://doi.org/10.1063/5.0176084openalex-snapshot

Analysis of image filtering based on different types of rank filters

inferred← citeshttps://doi.org/10.1109/icip46576.2022.9897288openalex-snapshot

NLCMAP: A Framework for the Efficient Mapping of Non-Linear Convolutional Neural Networks on FPGA Accelerators

inferred← citeshttps://doi.org/10.3389/fnagi.2021.720226openalex-snapshot

Deep Convolutional Neural Networks With Ensemble Learning and Generative Adversarial Networks for Alzheimer’s Disease Image Data Classification

inferred← citeshttps://doi.org/10.3389/fnume.2024.1380518openalex-snapshot

Mixture prior distributions and Bayesian models for robust radionuclide image processing

inferred← citeshttps://doi.org/10.3389/fnume.2025.1508816openalex-snapshot

Bayesian modeling with locally adaptive prior parameters in small animal imaging

inferred← citeshttps://doi.org/10.3390/electronics13030622openalex-snapshot

Seismic Event Detection in the Copahue Volcano Based on Machine Learning: Towards an On-the-Edge Implementation

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Block-Based Compression and Corresponding Hardware Circuits for Sparse Activations

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