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DiNAT-IR: Exploring Dilated Neighborhood Attention for High-Quality Image Restoration
Hanzhou Liu, Binghan Li, Chengkai Liu +1
Transformers, with their self-attention mechanisms for modeling long-range dependencies, have become a dominant paradigm in image restoration tasks. However, the high computational…
DeblurDiNAT: A Compact Model with Exceptional Generalization and Visual Fidelity on Unseen Domains
Hanzhou Liu, Binghan Li, Chengkai Liu +1
Recent deblurring networks have effectively restored clear images from the blurred ones. However, they often struggle with generalization to unknown domains. Moreover, these models…
Dilated Fully Convolutional Neural Network for Depth Estimation from a Single Image
Binghan Li, Yindong Hua, Yifeng Liu +1
Depth prediction plays a key role in understanding a 3D scene. Several techniques have been developed throughout the years, among which Convolutional Neural Network has recently ac…
Advanced Multiple Linear Regression Based Dark Channel Prior Applied on Dehazing Image and Generating Synthetic Haze
Binghan Li, Yindong Hua, Mi Lu
Haze removal is an extremely challenging task, and object detection in the hazy environment has recently gained much attention due to the popularity of autonomous driving and traff…
Multiple Linear Regression Haze-removal Model Based on Dark Channel Prior
Binghan Li, Wenrui Zhang, Mi Lu
Dark Channel Prior (DCP) is a widely recognized traditional dehazing algorithm. However, it may fail in bright region and the brightness of the restored image is darker than hazy i…