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Found 12 papers, 1 papers with code
ROI-Aware Multiscale Cross-Attention Vision Transformer for Pest Image Identification
More importantly, for the new challenging dataset IP102(CBSS) that contains only pest images with complex backgrounds and small sizes, the proposed model can maintain high recognition accuracy, whereas that of other SOTA models decrease sharply, demonstrating that our model is more robust to complex background and scale problems.
Trap-Based Pest Counting: Multiscale and Deformable Attention CenterNet Integrating Internal LR and HR Joint Feature Learning
In addition, the proposed model is confirmed to be effective in overcoming severe occlusions and variations in pose and scale.
Heavy Rain Face Image Restoration: Integrating Physical Degradation Model and Facial Component Guided Adversarial Learning
Unlike traditional image degradation models (IDM), such as rain removal and superresolution, this study addresses a new IDM referred to as a scale-aware heavy rain model and proposes a method for restoring high-resolution face images (HR-FIs) from low-resolution heavy rain face images (LRHR-FI).
New Encoder Learning for Captioning Heavy Rain Images via Semantic Visual Feature Matching
To achieve this, a target encoder is initially trained in an encoder-decoder framework to associate visual features with semantic words.
Layer Decomposition Learning Based on Gaussian Convolution Model and Residual Deblurring for Inverse Halftoning
For the detail layer, a new structure-aware residual deblurring subnetwork (SARDS) is presented.
Inverse Halftoning Through Structure-Aware Deep Convolutional Neural Networks
The subnetwork for the image structure prediction is trained using the mini-batch gradient descent algorithm given the halftoned patches and gradient patches, which are fed into the input and loss layers of the subnetwork, respectively.
Apple Leaf Disease Identification through Region-of-Interest-Aware Deep Convolutional Neural Network
The two subnetworks exhibit the architecture types of an encoder-decoder network and VGG network, respectively; subsequently, they are trained separately through transfer learning with a new training set containing class information, according to the types of leaf diseases and the ground truth images where the background, leaf area, and spot area are separated.
Multimodal Fusion via a Series of Transfers for Noise Removal
Different from conventional fusion approaches, the proposed method conducts a series of transfers: contrast, detail, and color transfers.
Near-Infrared Coloring via a Contrast-Preserving Mapping Model
This paper introduces a new coloring method to add colors to near-infrared gray images based on a contrast-preserving mapping model.
Rain structure transfer using an exemplar rain image for synthetic rain image generation
Next, residual rain patches are selected randomly, and then added to the given target image along a raster scanning direction.
Rain Removal via Shrinkage-Based Sparse Coding and Learned Rain Dictionary
Based on this error map, both the sparse codes of rain and non-rain dictionaries are used jointly to represent the image structures of objects and avoid the edge artifacts in the non-rain regions.
Near-Infrared Image Dehazing Via Color Regularization
It is also shown that the proposed color regularization can remove the edge artifacts which arise from the use of the conventional dark prior model.
