Proceedings ArticleDOI
Bit-depth expansion using Minimum Risk Based Classification
Gaurav Mittal,Vinit Jakhetiya,Sunil Prasad Jaiswal,Oscar C. Au,Anil Kumar Tiwari,Dai Wei +5 more
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TLDR
This paper proposes a novel method for bit- depth expansion which uses Minimum Risk Based Classification to create high bit-depth image and gives better objective (PSNR) and superior visual quality as compared to recently developedbit-depth expansion algorithms.Abstract:
Bit-depth expansion is an art of converting low bit-depth image into high bit-depth image. Bit-depth of an image represents the number of bits required to represent an intensity value of the image. Bit-depth expansion is an important field since it directly affects the display quality. In this paper, we propose a novel method for bit-depth expansion which uses Minimum Risk Based Classification to create high bit-depth image. Blurring and other annoying artifacts are lowered in this method. Our method gives better objective (PSNR) and superior visual quality as compared to recently developed bit-depth expansion algorithms.read more
Citations
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Journal ArticleDOI
Image Bit-Depth Enhancement via Maximum A Posteriori Estimation of AC Signal.
TL;DR: This work argues that a graph-signal smoothness prior-one defined on a graph embedding the image structure-is an appropriate prior for the bit-depth enhancement problem, and proposes an efficient approximation strategy that estimates the ac component of the desired signal in a maximum a posteriori formulation, efficiently computed via convex programming.
Journal ArticleDOI
IPAD: Intensity Potential for Adaptive De-Quantization.
TL;DR: This paper proposes a novel intensity potential field to model the complicated relationships among pixels, and an adaptive de-quantization algorithm is proposed to convert low bit-depth images to high bit- depth ones.
Journal ArticleDOI
Photo-realistic image bit-depth enhancement via residual transposed convolutional neural network
TL;DR: A novel neural network is proposed with summation and concatenation skip connections among transposed convolutional layers to cope with the gradient vanishing problem and outperforms state-of-the-art algorithms objectively and subjectively with suppressed false contour artifacts and preserved textures.
Journal ArticleDOI
Deep Reconstruction of Least Significant Bits for Bit-Depth Expansion
TL;DR: A deep residual network-based method based on the different properties of flat and non-flat areas, two channels are proposed to reconstruct these two kinds of areas, respectively and can further promote the subjective quality of the flat area.
Journal ArticleDOI
BE-CALF: Bit-Depth Enhancement by Concatenating All Level Features of DNN
TL;DR: A novel deep learning network is proposed based on the deep convolutional variational auto-encoders (VAEs), and skip connections that concatenate every two layers are applied to pass low-level and high-level features to consequent layers, easing the gradient vanishing problem.
References
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Inverse halftoning via MAP estimation
TL;DR: This paper focuses on the inverse problem, that of finding efficient techniques for reconstructing high-quality continuous-tone images from their halftoned versions, based on a maximum a posteriori (MAP) estimation criteria using a Markov random field (MRF) model for the prior image distribution.
Proceedings ArticleDOI
Bit-depth expansion by contour region reconstruction
TL;DR: A novel approach is proposed by considering the distance from contour edges, fine gradient value are applied to fill the contour gaps to achieve gradual transaction in bit-depth expansion algorithm.
Proceedings ArticleDOI
Pixel bit-depth increase by bit replication
Robert Ulichney,Shiufun Cheung +1 more
TL;DR: A simple and efficient method that uses bit replication, instead of conventional multiplication, to achieve bit replication expansion, and shows that the optimal number of repetitions is given by ceiling (m/q) and that the method is equivalent to multiplication by the ideal gain when m/q is an integer.
Proceedings ArticleDOI
Bit-depth expansion by adaptive filter
TL;DR: A novel, simple and efficient adaptive method to increase bit-depth taking advantage of the existing techniques to give superior image quality is proposed.
Proceedings ArticleDOI
Hybrid inverse halftoning using adaptive filtering
TL;DR: A novel fast inverse halftoning technique using a combination of spatial varying filtering and spatial invariant filtering is proposed, which is significantly simpler than most existing algorithms.