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Interactive image Restoration Using Inpainting and Denoising

Interactive Image Restoration Using Inpainting and Denoising

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Nidhi Arora Prem Kalra Department of Computer Science and Engineering Indian Institute of Technology New Delhi India

Digital in painting is the technique of filling in the missing regions of an image using information from the surrounding area in a visually indistinguishable way. In this paper, we try to improve the Exemplar based method by manipulating the values of various parameters like patch size, shape and size of the mask. We present an analysis of the impact of various geometric parameters on the quality of in painted images. image denoising refers to the removal of unwanted noise from the images. In most cases, the images which need to be in painted are noisy, which makes it necessary to eliminate noise and fill in the missing regions from neighboring pixels. Therefore, filling in of missing regions and removal of noise are the two very important topics in image processing. This paper also addresses the issue of performing both in painting and denoising simultaneously using two different approaches: pipelined approach and interleaved approach. The effectiveness of these approaches is demonstrated with a number of results on various images.

关键词： Noise reduction Shape Noise Noise measurement conferences image restoration

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Tobacco Plant Disease Dataset 14

Tobacco Plant Disease Dataset

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14th International conference on Digital image processing, ICDIP 2022

作者： Lin, Hong Tse, Rita Tang, Su-Kit Qiang, Zhenping Ou, Jinliang Pau, Giovanni Faculty of Applied Sciences Macao Polytechnic University China Engineering Research Centre of Applied Technology on Machine Translation and Artificial Intelligence of Ministry of Education Macao Polytechnic University China College of Big Data and Intelligent Engineering Southwest Forestry University KunMing China Department of Computer Science and Engineering-DISI University of Bologna Italy Samueli Computer Science Department University of California Los Angeles United States

ISBN: (纸本)9781510657564

Tobacco is a valuable plant in agricultural and commercial industry. Any disease infection to the plant may lower the harvest and interfere the operation of supply chain in the market. image-based deep learning methods are cutting-edge technologies that can facilitate the diagnosis of diseases efficiently and effectively when large-scale dataset is available for training. However, there is not a public dataset about tobacco currently. A comprehensive dataset is appealed to take advantage of deep learning methods in tobacco cultivation urgently. In this paper, we propose to create a specific dataset for tobacco diseases, called Tobacco Plant Disease Dataset (TPDD). 2721 tobacco leaf images are taken in field. The dataset serves for two purposes: disease classification and leaf detection. For classification, we identify 12 classes and provide two types of disease annotations: 1) Whole Leaf Section;2) Disease Fragment Section. For leaf detection, we provide two kinds of bounding box: rectangle bounding box and polygon bounding box. In addition, we conduct baseline experiments to illustrate the usefulness of TPDD: 1) using deep learning model to detect single disease and multiple diseases;2) using YOLO-v3 and Mask-RCNN to detect leaves. We hope that the dataset could support the tobacco industry, also be a benchmark in fine-grained vision classification. © 2022 SPIE.

关键词： computer vision

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Speeding up SIFT using GPU

Speeding up SIFT using GPU

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： Aniruddha Acharya K R. Venkatesh Babu Video Anaytics Laboratory Indian Institute of Science Bangalore India

Scale Invariant Feature Transform (SIFT) is one of the widely used interest point features. It has been successfully applied in various computer vision algorithms like object detection, object tracking, robotic mapping and large scale image retrieval. Although SIFT descriptors are highly robust towards scale and rotation variations, the high computational complexity of the SIFT algorithm inhibits its use in applications demanding real time response, and in algorithms dealing with very large scale databases. This paper presents a parallel implementation of SIFT on a GPU, where we obtain a speed of around 55 fps for a 640×480 image. One of the main contributions of our work is the novel combined kernel optimization that has led to a significant improvement of 21.79% in the execution speed. We compare our results with the existing implementations in the literature that accelerate SIFT, and find that our implementation has better speedup than the most of them.

关键词： graphics processing units Kernel Feature extraction Convolution Instruction sets Optimization Random access memory

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Parametric dictionaries and feature augmentation for continuous domain adaptation 14

Parametric dictionaries and feature augmentation for continu...

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9th indian conference on computer vision, graphics and image processing, ICVGIP 2014

作者： Shekhar, Sumit Shroff, Nitesh Chellappa, Rama Adobe Research Bangalore India Light Paolo Alto United States University of Maryland College Park United States

ISBN: (纸本)1595930361

In this paper, we study methods for learning classifiers for the case when there is a variation introduced by an underlying continuous parameter θ representing transformations like blur, pose, time, etc. First, we consider the task of learning dictionary-based representation for such cases. Sparse representations driven by data-derived dictionaries have produced state-of-the-art results in various image restoration and classification tasks. While significant advances have been made in this direction, most techniques have focused on learning a single dictionary to represent all variations in the data. In this paper, we show that dictionary learning can be significantly improved by explicitly parameterizing the dictionaries for θ. We develop an optimization framework to learn parametric dictionaries that vary smoothly with θ. We propose two optimization approaches, (a) least squares approach, and (b) the regularized K-SVD approach. Furthermore, we analyze the variations in data induced by θ from a different yet related perspective of feature augmentation. Specifically, we extend the feature augmentation technique proposed for adaptation of discretely separable domains to continuously varying domains, and propose a Mercer kernel to account for such changes. We present experimental validation of the proposed techniques using both synthetic and real datasets. Copyright 2014 ACM.

关键词： image reconstruction

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Supervised Hashing for Retrieval of Multimodal Biometric Data 1

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3rd Workshop on computer vision Applications, WCVA 2018, held in conjunction with the 11th indian conference on computer vision, graphics and image processing, ICVGIP 2018

作者： Sumesh, T.A. Namboodiri, Vinay Gupta, Phalguni Department of CSE Indian Institute of Technology Kanpur Kanpur India

ISBN: (数字)9789811513879

ISBN: (纸本)9789811513862

Biometric systems commonly utilize multi-biometric approaches where a person is verified or identified based on multiple biometric traits. However, requiring systems that are deployed usually require verification or identification from a large number of enrolled candidates. These are possible only if there are efficient methods that retrieve relevant candidates in a multi-biometric system. To solve this problem, we analyze the use of hashing techniques that are available for obtaining retrieval. We specifically based on our analysis recommend the use of supervised hashing techniques over deep learned features as a possible common technique to solve this problem. Our investigation includes a comparison of some of the supervised and unsupervised methods viz. Principal Component Analysis (PCA), Locality Sensitive Hashing (LSH), Locality-sensitive binary codes from shift-invariant kernels (SKLSH), Iterative quantization: A procrustean approach to learning binary codes (ITQ), Binary Reconstructive Embedding (BRE) and Minimum loss hashing (MLH) that represent the prevalent classes of such systems and we present our analysis for the following biometric data: Face, Iris, and Fingerprint for a number of standard datasets. The main technical contributions through this work are as follows: (a) Proposing Siamese network based deep learned feature extraction method (b) Analysis of common feature extraction techniques for multiple biometrics as to a reduced feature space representation (c) Advocating the use of supervised hashing for obtaining a compact feature representation across different biometrics traits. (d) Analysis of the performance of deep representations against shallow representations in a practical reduced feature representation framework. Through experimentation with multiple biometrics traits, feature representations, and hashing techniques, we can conclude that current deep learned features when retrieved using supervised hashing can be a standard pipeline adopte

关键词： Biometrics

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Accelerating Native Inference Model Performance in Edge Devices using TensorRT 6

Accelerating Native Inference Model Performance in Edge Devi...

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6th IEEE International conference on Recent Advances in Intelligent Computational Systems, RAICS 2024

作者： Gopiktrishna, P.B. Marion Lincy G, Rubell Rishekeeshan, Abhishek Deekshitha Indian Institute of Information Technology Computer Science and Engineering Kottayam India AiRender Technology Pvt Ltd Bangalore India

ISBN: (纸本)9798350381689

In this era, deep learning is becoming increasingly popular for solving real-world problems. Due to the extremely high processing power required to execute the most complex deep learning models, graphics processing Units(GPUs) and Neural processing Units(NPUs) are essential for optimal execution. However, it is challenging to incorporate these high-performance processors in every edge device like Internet of Things gadgets, embedded systems, and cell phones to execute complex inference models. Hence, deep learning models must be optimized to perform well on edge devices with limited memory, power, and computing capabilities. Optimizing the inference models to accelerate performance on edge devices is one of the significant issues facing, especially in complex computation domains like computer vision. TensorRT, introduced by NVIDIA, is one of the best approaches used to optimize inference models, by accelerating their performance on edge devices without compromising the model accuracy. It presents a variety of optimization approaches, including layer fusion, quantization, dynamic tensor memory management, kernel auto tuning, and precision calibration. Every optimization technique is contingent upon the problem that have selected and the use of all optimization approaches has the potential to significantly speed up the performance of an inference model. This paper explores how TensorRT accelerates the performance of inference models like ResNet-50, VGG16, and Inception V3 in an image classification problem, and the results are analyzed with its native inference models. The utilization of TensorRT in edge devices is expanding significantly, reflecting a growing scope for enhanced performance and efficiency in processing complex neural networks at the edge. © 2024 IEEE.

关键词： graphics processing unit

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Objective evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform 12

Objective evaluation of noisy multimodal medical image fusio...

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8th indian conference on computer vision, graphics and image processing, ICVGIP 2012

作者： Singh, Rajiv Khare, Ashish Department of Electronics and Communication University of Allahabad Allahabad India

ISBN: (纸本)9781450316606

Medical image fusion needs proper attention as images obtained from medical instruments are of poor contrast and corrupted by blur and noise due to imperfection of image capturing devices. Thus, objective evaluation of medical image fusion techniques has become an important task in noisy domain. Therefore, in the present work, we have proposed maximum selection and energy based fusion rules for the evaluation of noisy multimodal medical image fusion using Daubechies complex wavelet transform (DCxWT). Unlike, traditional real valued wavelet transforms, which suffered from shift sensitivity and did not provide any phase information, DCxWT is shift invariant and provides phase information through its imaginary coefficients. Shift invariance and availability of phase information properties of DCxWT have been found useful for fusion of multimodal medical images. The experiments have been performed over several set of noisy medical images at multiple levels of noise for the proposed fusion scheme. Further, the proposed fusion scheme has been tested up to the maximum level of Gaussian, salt & pepper and speckle noise. Objective evaluation of the proposed fusion scheme is performed with fusion factor, fusion symmetry, entropy, standard deviation and edge information metrics. Results have been shown for two sets of multimodal medical images for the proposed method with maximum and energy based fusion rules, and comparison has been done with Lifting wavelet transform (LWT) and Stationary wavelet transform (SWT) based fusion methods. Comparative analysis of the proposed method with LWT and SWT based fusion methods at different noise levels shows the superiority of the proposed scheme. Moreover, the plots of different fusion metrics against the maximum level of Gaussian, salt & pepper and speckle noise show the robustness of the proposed fusion method against noise. © 2012 ACM.

关键词： image fusion

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A hybrid deep architecture for face recognition in real-life scenario 10

A hybrid deep architecture for face recognition in real-life...

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10th indian conference on computer vision, graphics and image processing, ICVGIP 2016

作者： Sanya, A. Bhattacharya, U. Parui, S.K. Department of CSE Indian Institute of Technology Kanpur India CVPR Unit Indian Statistical Institute Kolkata India

This article describes our recent study of a real-life face recognition problem using a hybrid architecture consisting of a very deep convolution neural network (CNN) and a support vector machine (SVM). The novel aspects of this study include (i) implementation of a really deep CNN architecture consisting of 11 layers to study the effect of increasing depth on recognition performance by a subsequent SVM, and (ii) verification of the recognition performance of this hybrid classifier trained by samples of a certain standard size on test face images of smaller sizes reminiscent to various real-life scenarios. Results of the present study show that the features computed at various shallow levels of a deep architecture have identical or at least comparable performances and are more robust than the deepest feature computed at the inner most sub-sampling layer. We have also studied a simple strategy of recognizing face images of very small sizes using this hybrid architecture trained by standard size face images and the recognition performance is reported. We obtained simulation results using the cropped images of the standard extended Yale Face Database which show an interesting characteristic of the proposed architecture with respect to face images captured in a very low intensity lighting condition. © Springer International Publishing AG 2017.

关键词： Face recognition

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Degradation Aware Multi-Scale Approach to No Reference image Quality Assessment 14

Degradation Aware Multi-Scale Approach to No Reference Image...

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14th indian conference on computer vision, graphics and image processing, ICVGIP 2023

作者： Roheda, Siddharth Unde, Amit Rashid, Loay Ameta, Abhishek Samsung Research Institute Bangalore India

ISBN: (纸本)9798400716256

With the advent of smartphones and social media, images have become a popular medium for sharing information. As a result understanding the perceived quality of images has gained importance. In the recent years No-Reference image Quality Assessment (NR-IQA) has gained prominence due to the abundance of User Generated Content (UGC) being consumed. Applications ranging from photo capture assistance, suggestion of best images for sharing, creation of stories from user media gallery, and enhancement of images for best user experience require NR-IQA. This paper presents a novel Degradation Aware Multi-Scale NR-IQA technique that leverages the multi-scale nature of feature extraction in the Human Visual System (HVS) and also aims at understanding the perceived quality of images across various distortions. This is achieved via learning of two auxiliary tasks: (i) Identification of distortions present in images;(ii) Learning to rank images across distortions. Additionally, a unique data generation strategy is introduced to generate realistic distortions for learning cross-distortion ranking of image quality. The proposed approach achieves State-Of-The-Art (SOTA) performance on multiple IQA datasets and demonstrates generalization in cross-dataset testing. The results showcase the efficacy of the proposed method in identifying and ranking the quality of images across various distortions, making it a promising approach for NR-IQA in real-world applications. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： image enhancement

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Restoration of Partial Color Artifact and Blotches using histogram matching and sparse technique

Restoration of Partial Color Artifact and Blotches using his...

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National conference on computer vision, Pattern Recognition, image processing and graphics (NCVPRIPG)

作者： V Narendra Sumana Gupta Department of Electrical Engineering Indian Institute of Technology Kanpur

In this paper we have proposed methods for restoration of artifacts called Partial Color Artifact(PCA) and Blotches which appear frequently in old video films. The PCA occurs due to partial loss of information in the upper color layers of the video film. As the inner most color layer is unaffected, the information present in this inner most color layer of the film aids in the reconstruction of damaged pixels from previously reconstructed frames. In Blotch artifact the pixel information is completely lost. The proposed Blotch reconstruction method is based on sparse recovery of signals from small number of measurements. Our blotch reconstruction process is computationally efficient because the image is segmented into non overlapping blocks and reconstruction is done block wise.

关键词： image reconstruction image color analysis Principal component analysis image restoration Reconstruction algorithms Vectors Optimization

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