Z
Ziaur Rahman
Researcher at Sichuan University
Publications - 7
Citations - 84
Ziaur Rahman is an academic researcher from Sichuan University. The author has contributed to research in topics: Object detection & Filter (signal processing). The author has an hindex of 4, co-authored 7 publications receiving 38 citations. Previous affiliations of Ziaur Rahman include Huanggang Normal University.
Papers
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Journal ArticleDOI
A framework for fast automatic image cropping based on deep saliency map detection and gaussian filter
TL;DR: A deep learning strategy is adopted to train a large data-set of images, to get saliency map from the input image using graph-based segmentation and gray level adjustment to enhance and extract more accurate and clear Saliency map.
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A framework for efficient brain tumor classification using MRI images.
Yurong Guan,Muhammad Aamir,Ziaur Rahman,Ammara Ali,Waheed Ahmed Abro,Zaheer Ahmed Dayo,Muhammad Shoaib Bhutta,Zhihua Hu +7 more
TL;DR: In this paper, the first brain MRI image is pre-processed to improve its visual quality and increase sample images to avoid overfitting in the network, and the tumor proposals or locations are obtained based on the agglomerative clustering-based method.
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Structure revealing of low-light images using wavelet transform based on fractional-order denoising and multiscale decomposition
TL;DR: A new model is proposed to prevent overenhancement, handle uneven illumination, and suppress noise in underexposed images and achieves high efficacy and outperforms the traditional approaches in terms of overall performance.
Journal Article
A Hybrid Proposed Framework for Object Detection and Classification
Muhammad Aamir,Yi-Fei Pu,Ziaur Rahman,Waheed Ahmed Abro,Hamad Naeem,Farhan Ullah,Aymen Mudheher Badr +6 more
TL;DR: A methodology to detect and classify the image's pixels' locations using enhanced bag of words (BOW) and it is proved that it gave the better classification results for the non-rigid classes.
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Efficient Image Enhancement Model for Correcting Uneven Illumination Images
TL;DR: This paper presents an automatic image enhancement method, capable of producing quality results for all types of images captured under uneven exposure conditions, and reveals the effectives of the proposed approach as compared to other state-of-the-art algorithms.