Proceedings ArticleDOI
Observation model based perceptually motivated bilateral filter for image reconstruction
Vinit Jakhetiya,Weisi Lin,Sunil Prasad Jaiswal,Anil Kumar Tiwari,Sharath Chandra Guntuku +4 more
- pp 201-205
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TLDR
From experimental results, it is validated that the proposed algorithm has the capability of reconstructing sharp edges, as compared to existing non-linear filtering algorithms.Abstract:
Recently, a lot of bilateral and non-local means based reconstruction algorithms are proposed in literature. The success of these filters lies in finding similar patches in the neighbourhood. However, in complex regions and in presence of noise, finding similar patches based on mean square error (MSE) is not reliable. This results into blurred edges and visible patches in the reconstructed image. To address this issue, we propose a new Observation model based and Perceptually Motivated Bilateral Filter (OPBIF) for Image Reconstruction. In which image quality assessment (IQA) matrices are used to find the similarity among the patches. From experimental results, it is validated that the proposed algorithm has the capability of reconstructing sharp edges, as compared to existing non-linear filtering algorithms.read more
Citations
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Journal ArticleDOI
Complex wavelet based quality assessment for AS-OCT images with application to Angle Closure Glaucoma diagnosis
Swamidoss Issac Niwas,Vinit Jakhetiya,Weisi Lin,Chee Keong Kwoh,Chelvin C A Sng,Maria Cecilia Aquino,Koh Victor,Paul T.K. Chew +7 more
TL;DR: The proposed quality index has the ability of automatic objective and quantitative assessment of AS-OCT image quality and this quality index is similar to glaucoma specialist.
Proceedings ArticleDOI
Optimized high-frequency based interpolation for multispectral demosaicking
TL;DR: This work proposes to incorporate two different high-frequency components and integrate them optimally in the linear minimum mean square sense (LMMSE) for the precise reconstruction of undersampled components and achieves superior performance compared to existing algorithms both in terms of objective and subjective quality.
Journal ArticleDOI
Synthesis and analysis of prediction errors and error fusion based prior for prediction algorithms
TL;DR: This work forms the post-processing stage as a Maximum-a-Posteriori (MAP) estimation problem and finds that prediction errors of a missing pixel and its neighboring pixels are also correlated, which can be utilized to improve the prediction accuracy.
References
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Proceedings ArticleDOI
Bilateral filtering for gray and color images
Carlo Tomasi,Roberto Manduchi +1 more
TL;DR: In contrast with filters that operate on the three bands of a color image separately, a bilateral filter can enforce the perceptual metric underlying the CIE-Lab color space, and smooth colors and preserve edges in a way that is tuned to human perception.
Proceedings ArticleDOI
A non-local algorithm for image denoising
TL;DR: A new measure, the method noise, is proposed, to evaluate and compare the performance of digital image denoising methods, and a new algorithm, the nonlocal means (NL-means), based on a nonlocal averaging of all pixels in the image is proposed.
Journal ArticleDOI
Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures
Zhou Wang,Alan C. Bovik +1 more
TL;DR: This article has reviewed the reasons why people want to love or leave the venerable (but perhaps hoary) MSE and reviewed emerging alternative signal fidelity measures and discussed their potential application to a wide variety of problems.
Journal ArticleDOI
Kernel Regression for Image Processing and Reconstruction
TL;DR: This paper adapt and expand kernel regression ideas for use in image denoising, upscaling, interpolation, fusion, and more and establishes key relationships with some popular existing methods and shows how several of these algorithms are special cases of the proposed framework.
Journal ArticleDOI
Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Index
TL;DR: It is found that the pixel-wise gradient magnitude similarity (GMS) between the reference and distorted images combined with a novel pooling strategy-the standard deviation of the GMS map-can predict accurately perceptual image quality.
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