Covering image processing and computer vision concepts with small c++ programs ranging from reading an image, histogram manipulation to object tracking.
-
Updated
Sep 14, 2021 - C++
Covering image processing and computer vision concepts with small c++ programs ranging from reading an image, histogram manipulation to object tracking.
CONSAC: Robust Multi-Model Fitting by Conditional Sample Consensus (Code @fkluger)
ReLaX-VQA: Residual Fragment and Layer Stack Extraction for Enhancing Video Quality Assessment
Temporally Consistent Horizon Lines (Code @fkluger)
Deep Denoising Network in Frequency Domain for Hyperspectral Image
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution (official repository)
Splitwise System Design
[TVT 2023] Haze Visibility Enhancement for Promoting Traffic Situational Awareness in Vision-Enabled Intelligent Transportation
Low-Light Image Enhancement
[TAI 2023] Blind Image Despeckling Using Multi-Scale Attention-Guided Neural Network
KITTI dataset horizon line extension proposed in the paper "Temporally Consistent Horizon Lines" (Code @fkluger)
This paper is accepted by IEEE TCSVT
low level computer vision 任务研究的一些阅读感想记录
Inference code for "Unified Multi-Weather Transformer for Multi-Weather Image Restoration".
Spatial-Spectral Quasi-Attention Recurrent Network for Hyperspectral Image Denoising.
Data Upcycling Knowledge Distillation for Image Super-Resolution (official repository)
Source code for book "Image algorithms for low-level vision tasks" (Jia. 2024), including denoising, super-resolution, dehazing, image composition and enhancement models and algorithms implemented in pure Python.
[ACM MM 2024] Dual-Hybrid Attention Network for Specular Highlight Removal
An official source code of AAAI 2023 paper, "Robust Image Denoising of No-Flash Images Guided by Consistent Flash Images".
I Can See Clearly Now : Image Restoration via De-Raining unofficial code implementation(pytorch)
Add a description, image, and links to the low-level-vision topic page so that developers can more easily learn about it.
To associate your repository with the low-level-vision topic, visit your repo's landing page and select "manage topics."