A Comprehensive Overview of Deep Learning Applications in Computer Vision

The author, Dr. Huang Yu, presents a broad survey of how deep learning, especially convolutional neural networks (CNNs), has transformed computer vision across many domains.

Historical Background

Starting from Geoffrey Hinton's 2006 breakthrough, the field moved from early restricted Boltzmann machines to the landmark AlexNet (2012) that won ImageNet, followed by a rapid succession of models such as ZFNet, VGG, GoogLeNet/Inception, ResNet, DenseNet, SE‑Net, and many others, each improving depth, efficiency, or architectural innovation.

Core Image/Video Processing Tasks

Typical low‑level tasks—denoising, dehazing, deblurring, and artifact reduction—are now tackled with encoder‑decoder networks (e.g., AR‑CNN). Super‑resolution and enhancement use CNNs inspired by bilateral filtering or directly learn high‑frequency residuals. Inpainting, colorization, and other restoration problems also rely on GAN‑based encoder‑decoder designs.

Feature Extraction and Pre‑processing

Traditional hand‑crafted features (SIFT, SURF, Bag‑of‑Words) have largely been replaced by learned CNN descriptors. Models such as LIFT mimic SIFT, while modern edge/contour detectors employ encoder‑decoder networks to produce dense boundary maps.

2.5‑D Vision

Tasks that involve motion or disparity—optical flow, depth estimation, video de‑interlacing, and frame‑rate up‑conversion—are now solved with deep networks (e.g., FlowNet, hourglass‑style flow estimators, MEMC‑CNN). Inverse warping techniques enable novel‑view synthesis from monocular depth predictions.

3‑D Reconstruction and SLAM

Multi‑view stereo (MVS) and structure‑from‑motion (SfM) pipelines have been re‑implemented with CNNs (e.g., MVSNet, 3D‑R2N2). SLAM systems combine visual odometry, loop‑closure detection, and bundle adjustment, with recent deep variants such as CNN‑SLAM, VIO networks, and LiDAR‑camera calibration nets (CalibNet).

High‑Level Understanding

Semantic and instance segmentation are dominated by Fully Convolutional Networks (FCN) and Mask R‑CNN families. Object detection progressed from R‑CNN → Fast/​Faster R‑CNN to one‑stage detectors (SSD, YOLO, RetinaNet). Pose estimation uses Part Affinity Fields, while tracking (single‑ and multi‑object) leverages both CNN and RNN architectures.

Application Domains

Deep vision powers content‑based image retrieval, augmented reality (AR) pipelines (feature‑based relocalization, camera‑motion estimation), image captioning, and visual question answering. The article also lists numerous representative models and system diagrams for each sub‑task.

Conclusion

Overall, the survey demonstrates that deep learning has become the unifying framework for virtually every computer‑vision problem, replacing many classical hand‑crafted pipelines and enabling new capabilities in AR, autonomous driving, and intelligent perception.