Low Sub-band Image Research Articles

Dark satellite image enhancement using knee transfer function and gamma correction based on DWT–SVD

In this paper, an improved and simple approach for enhancement of dark and low contrast satellite image based on knee function and gamma correction using discrete wavelet transform with singular value decomposition (DWT---SVD) has been proposed for quality enhancement of feature. In addition, this method can also process the high resolution dark or very low contrast images, and offers best enhanced result using tuning parameter of Gamma. The technique decomposes the input image into four frequency subbands by using DWT and estimates the singular value matrix of the low---low subband image, and then compute the knee transfer function using gamma correction for further improvement of the LL component. Afterward, processed LL band image undergoes IDWT together with the unprocessed LH, HL, and HH subbands to generate an appropriate enhanced image. Although, various histogram equalization approaches has been proposed in the literature, they tend to degrade the overall image quality by exhibiting saturation artifacts in both low- and high-intensity regions. The proposed algorithm overcomes this problem using knee function and gamma correction. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than the existing techniques.

Mammogram Enhancement Using Lifting Dyadic Wavelet Transform and Normalized Tsallis Entropy

In this paper, we present a new technique for mammogram enhancement using fast dyadic wavelet transform (FDyWT) based on lifted spline dyadic wavelets and normalized Tsallis entropy. First, a mammogram image is decomposed into a multiscale hierarchy of low-subband and high-subband images using FDyWT. Then noise is suppressed using normalized Tsallis entropy of the local variance of the modulus of oriented high-subband images. After that, the wavelet coefficients of high-subbands are modified using a non-linear operator and finally the low-subband image at the first scale is modified with power law transformation to suppress background. Though FDyWT is shift-invariant and has better potential for detecting singularities like edges, its performance depends on the choice of dyadic wavelets. On the other hand, the number of vanishing moments is an important characteristic of dyadic wavelets for singularity analysis because it provides an upper bound measurement for singularity characterization. Using lifting dyadic schemes, we construct lifted spline dyadic wavelets of different degrees with increased number of vanishing moments. We also examine the effect of these wavelets on mammogram enhancement. The method is tested on mammogram images, taken from MIAS (Mammographic Image Analysis Society) database, having various background tissue types and containing different abnormalities. The comparison with the state-of-the-art contrast enhancement methods reveals that the proposed method performs better and the difference is statistically significant.

Low‐contrast satellite images enhancement using discrete cosine transform pyramid and singular value decomposition

This study presents a satellite image contrast enhancement technique based on the discrete cosine transform (DCT) pyramid and singular value decomposition (SVD), in contrast to the methods based on wavelet decomposition and SVD which could fail to produce satisfactory results for some low-contrast images. With the proposed method, an input image is decomposed into a low sub-band image and reversed L-shape blocks containing the high-frequency coefficients of the DCT pyramid. The singular value matrix of the equalised low sub-band image is then estimated from the combination between the singular matrix of the low sub-band image and the singular matrix of its global histogram equalisation. The qualitative and quantitative performances of the proposed technique are compared with those of conventional image equalisation such as general histogram equalisation and local histogram equalisation, as well as some state-of-the-art techniques such as singular value equalisation technique. Moreover, the proposed technique is contrasted against the technique based on the discrete wavelet transform (DWT) and SVD (DWT-SVD) as well as the technique based on DCT-SVD. The experimental results show that the proposed method outperforms both conventional and the state-of-the-art techniques.