Search Results for author: Saeed Anwar

Found 39 papers, 28 papers with code

DRT: A Lightweight Single Image Deraining Recursive Transformer

1 code implementation25 Apr 2022 Yuanchu Liang, Saeed Anwar, Yang Liu

Over parameterization is a common technique in deep learning to help models learn and generalize sufficiently to the given task; nonetheless, this often leads to enormous network structures and consumes considerable computing resources during training.

Image Restoration Single Image Deraining

Visual Attention Methods in Deep Learning: An In-Depth Survey

no code implementations16 Apr 2022 Mohammed Hassanin, Saeed Anwar, Ibrahim Radwan, Fahad S Khan, Ajmal Mian

Inspired by the human cognitive system, attention is a mechanism that imitates the human cognitive awareness about specific information, amplifying critical details to focus more on the essential aspects of data.

Deep Attention

Pyramidal Attention for Saliency Detection

1 code implementation14 Apr 2022 Tanveer Hussain, Abbas Anwar, Saeed Anwar, Lars Petersson, Sung Wook Baik

Consequently, we present a new SOD perspective of generating RGB-D SOD without acquiring depth data during training and testing and assist RGB methods with depth clues for improved performance.

object-detection Object Detection +2

Vehicle and License Plate Recognition with Novel Dataset for Toll Collection

1 code implementation11 Feb 2022 Muhammad Usama, Hafeez Anwar, Muhammad Muaz Shahid, Abbas Anwar, Saeed Anwar, Helmuth Hlavacs

The best Mean Average Precision (mAP@0. 5) of 98. 8% for vehicle type recognition, 98. 5% for license plate detection, and 98. 3% for license plate reading is achieved by YOLOv4, while its lighter version, i. e., Tiny YOLOv4 obtained a mAP of 97. 1%, 97. 4%, and 93. 7% on vehicle type recognition, license plate detection, and license plate reading, respectively.

License Plate Detection License Plate Recognition

Image Dehazing Transformer With Transmission-Aware 3D Position Embedding

no code implementations CVPR 2022 Chun-Le Guo, Qixin Yan, Saeed Anwar, Runmin Cong, Wenqi Ren, Chongyi Li

Though Transformer has occupied various computer vision tasks, directly leveraging Transformer for image dehazing is challenging: 1) it tends to result in ambiguous and coarse details that are undesired for image reconstruction; 2) previous position embedding of Transformer is provided in logic or spatial position order that neglects the variational haze densities, which results in the sub-optimal dehazing performance.

Image Dehazing Image Reconstruction +1

PU-Transformer: Point Cloud Upsampling Transformer

no code implementations24 Nov 2021 Shi Qiu, Saeed Anwar, Nick Barnes

Given the rapid development of 3D scanners, point clouds are becoming popular in AI-driven machines.

PnP-3D: A Plug-and-Play for 3D Point Clouds

1 code implementation16 Aug 2021 Shi Qiu, Saeed Anwar, Nick Barnes

With the help of the deep learning paradigm, many point cloud networks have been invented for visual analysis.

object-detection Object Detection +1

Investigating Attention Mechanism in 3D Point Cloud Object Detection

1 code implementation2 Aug 2021 Shi Qiu, Yunfan Wu, Saeed Anwar, Chongyi Li

Object detection in three-dimensional (3D) space attracts much interest from academia and industry since it is an essential task in AI-driven applications such as robotics, autonomous driving, and augmented reality.

Autonomous Driving object-detection +1

Disentangling Noise from Images: A Flow-Based Image Denoising Neural Network

1 code implementation11 May 2021 Yang Liu, Saeed Anwar, Zhenyue Qin, Pan Ji, Sabrina Caldwell, Tom Gedeon

The prevalent convolutional neural network (CNN) based image denoising methods extract features of images to restore the clean ground truth, achieving high denoising accuracy.

Disentanglement Image Denoising

Fusing Higher-Order Features in Graph Neural Networks for Skeleton-Based Action Recognition

1 code implementation4 May 2021 Zhenyue Qin, Yang Liu, Pan Ji, Dongwoo Kim, Lei Wang, Bob McKay, Saeed Anwar, Tom Gedeon

Recent skeleton-based action recognition methods extract features from 3D joint coordinates as spatial-temporal cues, using these representations in a graph neural network for feature fusion to boost recognition performance.

Action Recognition Skeleton Based Action Recognition

Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding

3 code implementations27 Apr 2021 Chongyi Li, Saeed Anwar, Junhui Hou, Runmin Cong, Chunle Guo, Wenqi Ren

As a result, our network can effectively improve the visual quality of underwater images by exploiting multiple color spaces embedding and the advantages of both physical model-based and learning-based methods.

Ranked #2 on Underwater Image Restoration on LSUI (using extra training data)

Image Enhancement Underwater Image Restoration

Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion

2 code implementations CVPR 2021 Shi Qiu, Saeed Anwar, Nick Barnes

Given the prominence of current 3D sensors, a fine-grained analysis on the basic point cloud data is worthy of further investigation.

3D Semantic Segmentation

Densely Deformable Efficient Salient Object Detection Network

1 code implementation12 Feb 2021 Tanveer Hussain, Saeed Anwar, Amin Ullah, Khan Muhammad, Sung Wook Baik

In this paper, inspired by the best background/foreground separation abilities of deformable convolutions, we employ them in our Densely Deformable Network (DDNet) to achieve efficient SOD.

Ranked #2 on RGB-D Salient Object Detection on SIP (Average MAE metric, using extra training data)

object-detection RGB-D Salient Object Detection +3

Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable

1 code implementation7 Sep 2020 Yang Liu, Zhenyue Qin, Saeed Anwar, Sabrina Caldwell, Tom Gedeon

Identifying the information lossless condition for deep neural architectures is important, because tasks such as image restoration require keep the detailed information of the input data as much as possible.

Image Denoising Image Inpainting +1

Uncertainty Inspired RGB-D Saliency Detection

4 code implementations7 Sep 2020 Jing Zhang, Deng-Ping Fan, Yuchao Dai, Saeed Anwar, Fatemeh Saleh, Sadegh Aliakbarian, Nick Barnes

Our framework includes two main models: 1) a generator model, which maps the input image and latent variable to stochastic saliency prediction, and 2) an inference model, which gradually updates the latent variable by sampling it from the true or approximate posterior distribution.

RGB-D Salient Object Detection RGB Salient Object Detection +1

Image Colorization: A Survey and Dataset

1 code implementation25 Aug 2020 Saeed Anwar, Muhammad Tahir, Chongyi Li, Ajmal Mian, Fahad Shahbaz Khan, Abdul Wahab Muzaffar

Image colorization is the process of estimating RGB colors for grayscale images or video frames to improve their aesthetic and perceptual quality.

Colorization

Attention Based Real Image Restoration

no code implementations26 Apr 2020 Saeed Anwar, Nick Barnes, Lars Petersson

Furthermore, the evaluation in terms of quantitative metrics and visual quality for four restoration tasks i. e. Denoising, Super-resolution, Raindrop Removal, and JPEG Compression on 11 real degraded datasets against more than 30 state-of-the-art algorithms demonstrate the superiority of our R$^2$Net.

Denoising Image Restoration +2

Identity Enhanced Residual Image Denoising

1 code implementation26 Apr 2020 Saeed Anwar, Cong Phuoc Huynh, Fatih Porikli

We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules and residual on the residual architecture for image denoising.

Image Denoising

Mosaic Super-resolution via Sequential Feature Pyramid Networks

no code implementations15 Apr 2020 Mehrdad Shoeiby, Mohammad Ali Armin, Sadegh Aliakbarian, Saeed Anwar, Lars Petersson

Additionally, to the best of our knowledge, our method is the first specialized method to super-resolve mosaic images, whether it be multi-spectral or Bayer.

Astronomy Autonomous Driving +1

A Systematic Evaluation: Fine-Grained CNN vs. Traditional CNN Classifiers

1 code implementation24 Mar 2020 Saeed Anwar, Nick Barnes, Lars Petersson

In this work, we investigate the performance of the landmark general CNN classifiers, which presented top-notch results on large scale classification datasets, on the fine-grained datasets, and compare it against state-of-the-art fine-grained classifiers.

General Classification

Multi-FAN: Multi-Spectral Mosaic Super-Resolution Via Multi-Scale Feature Aggregation Network

no code implementations17 Sep 2019 Mehrdad Shoeiby, Sadegh Aliakbarian, Saeed Anwar, Lars Petersson

This mosaic image is then merged with the mosaic image generated by the SR network to produce a quantitatively superior image.

Super-Resolution

Deep Ancient Roman Republican Coin Classification via Feature Fusion and Attention

1 code implementation26 Aug 2019 Hafeez Anwar, Saeed Anwar, Sebastian Zambanini, Fatih Porikli

We perform the classification of ancient Roman Republican coins via recognizing their reverse motifs where various objects, faces, scenes, animals, and buildings are minted along with legends.

Classification General Classification

Diving Deeper into Underwater Image Enhancement: A Survey

no code implementations17 Jul 2019 Saeed Anwar, Chongyi Li

In this paper, our main aim is two-fold, 1): to provide a comprehensive and in-depth survey of the deep learning-based underwater image enhancement, which covers various perspectives ranging from algorithms to open issues, and 2): to conduct a qualitative and quantitative comparison of the deep algorithms on diverse datasets to serve as a benchmark, which has been barely explored before.

Image Enhancement

Densely Residual Laplacian Super-Resolution

1 code implementation28 Jun 2019 Saeed Anwar, Nick Barnes

Super-Resolution convolutional neural networks have recently demonstrated high-quality restoration for single images.

Image Super-Resolution

A Deep Journey into Super-resolution: A survey

2 code implementations16 Apr 2019 Saeed Anwar, Salman Khan, Nick Barnes

Deep convolutional networks based super-resolution is a fast-growing field with numerous practical applications.

Image Super-Resolution Single Image Super Resolution

Real Image Denoising with Feature Attention

3 code implementations ICCV 2019 Saeed Anwar, Nick Barnes

Deep convolutional neural networks perform better on images containing spatially invariant noise (synthetic noise); however, their performance is limited on real-noisy photographs and requires multiple stage network modeling.

Color Image Denoising Image Denoising

Deep Underwater Image Enhancement

2 code implementations10 Jul 2018 Saeed Anwar, Chongyi Li, Fatih Porikli

In an underwater scene, wavelength-dependent light absorption and scattering degrade the visibility of images, causing low contrast and distorted color casts.

Image Enhancement

Chaining Identity Mapping Modules for Image Denoising

no code implementations8 Dec 2017 Saeed Anwar, Cong Phouc Huynh, Fatih Porikli

We propose to learn a fully-convolutional network model that consists of a Chain of Identity Mapping Modules (CIMM) for image denoising.

Image Denoising

Class-Specific Image Deblurring

no code implementations ICCV 2015 Saeed Anwar, Cong Phuoc Huynh, Fatih Porikli

In image deblurring, a fundamental problem is that the blur kernel suppresses a number of spatial frequencies that are difficult to recover reliably.

Deblurring Image Deblurring

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