Adaptive contourlet-wavelet iterative shrinkage/thresholding for remote sensing image restoration

Wen, Nu; Yang, Shu; Zhu, Ce; Cui, Shengcheng

doi:10.1631/jzus.c1300377

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A new representation of image without redundancy called Wavelet Based Contourlet Transform (WBCT) was proposed . WBCT has advantages of both wavelet and contourlet transform approaches and has several applications such as interactive shrinkage for visual quality recognition, remote sensing image restoration, image compression, image enhancement, compressed sensing, and so forth. These transform based compression processes do not know the diagnostically important area of the medical image.…”

Section: Introductionmentioning

confidence: 99%

A hybrid contextual compression technique using wavelet and contourlet transforms with PSO optimized prediction

Sophia

Anitha

2017

Int J Imaging Syst Tech

View full text Add to dashboard Cite

Contextual compression is an essential part of any medical image compression since it facilitates no loss of diagnostic information. Although there are many techniques available for contextual image compression still there is a need for developing an efficient and optimized technique which would produce good quality images at lower bit rates. This article presents an efficient contextual compression algorithm using wavelet and contourlet transforms to capture the fine details of the image, along with directional information to produce good quality at high Compression Ratio (CR). The 2D discrete wavelet transform, which uses the simplest Daubechies wavelets, db1, or haar wavelet, is chosen and used to get the subband coefficients. The approximate coefficients of the higher subbands undergo contourlet transform employing length N ladder filters for capturing the directional information of the subbands at different scale and orientations. An optimized approach is used for predicting the quantized and the normalized subband coefficients resulting in improved compression performance. The proposed contextual compression approach was evaluated for its performance in terms of CR, Peak Signal to Noise Ratio, Feature SIMilarity index, Structure SIMilarity Index, and Universal quality (Q) after reconstruction. The results clarify the efficiency of the proposed method over other compression techniques. K E Y W O R D Scontextual compression, contourlet transform, magnetic resonance imaging brain image, optimized prediction, wavelet transform Int.

show abstract