Weighted contrast enhancement based enhancement for remote sensing images

This paper discuss a novel approach based on dominant brightness level analysis and adaptive intensity transformation to enhance the contrast for remote sensing images. In this approach  we first perform discrete wavelet (DWT) on the input images and then decompose the bLL sub band into low-, middle-, and high-intensity layers using the log-average luminance. After estimating the intensity transformation, the resulting enhanced image is obtained by using the inverse DWT. The proposed algorithm overcomes this problem using the adaptive intensity transfer function. The experimental results show that the proposed algorithm enhances the overall contrast and visibility of local details better than existing techniques

Blood vessel enhancement via multi-dictionary and sparse coding: Application to retinal vessel enhancing

International audienceBlood vessel images can provide considerable information of many diseases, which are widely used by ophthalmologists for disease diagnosis and surgical planning. In this paper, we propose a novel method for the blood Vessel Enhancement via Multi-dictionary and Sparse Coding (VE-MSC). In the proposed method, two dictionaries are utilized to gain the vascular structures and details, including the Representation Dictionary (RD) generated from the original vascular images and the Enhancement Dictionary (ED) extracted from the corresponding label images. The sparse coding technology is utilized to represent the original target vessel image with RD. After that, the enhanced target vessel image can be reconstructed using the obtained sparse coefficients and ED. The proposed method has been evaluated for the retinal vessel enhancement on the DRIVE and STARE databases. Experimental results indicate that the proposed method can not only effectively improve the image contrast but also enhance the retinal vascular structures and details

Pixel Classification Algorithms for Noise Removal and Signal Preservation in Low Pass Filtering

Contrast enhancement is essential to improve the image quality in most of image pre-processing. A histogram equalization process can be used to achieve a high contrast. It causes, however, also noise generation. Involving a low-pass filtering process is an effective way to achieve a high-quality contrast enhancement with low-noise, but it leads to the conflict between noise removal and signal preservation. To perform discriminative low-pass filtering operations with the presence of noises and signal variations in different regions, it is thus necessary to develop good algorithms to classify the pixels. In this thesis, two classification algorithms are proposed. They aim at low-contrast images where gradient signals are severely degraded by various causes during the acquisition process. They are to classify the pixels according to the initial gray-level homogeneity of their regions. The basic classification method is done by gradient thresholding, and the threshold values are generated by means of gradient distribution analysis. To tackle the problems of various gradient degradation patterns in low-contrast images, image pixels are grouped in a particular way that, in the same group, pixels in homogeneous regions can be easily distinguished from those in non-homogeneous regions by the basic method of simple gradient thresholding. Two algorithms based on different grouping methods are proposed. The first algorithm aims at high dynamic range images. The pixels are first grouped according to their gray-level ranges, as the gradient degradation is, in such a case, gray-level-dependent. The gradient distribution of each sub-range is obtained and a pixel classification is then made to adapt to their original gray-level signals in the sub-range. The other algorithm is to tackle a wider range of low-contrast images. In this algorithm, a gray-level histogram thresholding is performed to divide the pixels into two groups according to their likelihood to homogeneous, or non-homogeneous, pixels. Thus, in one group a majority of homogeneous pixels is established and in the other group the majority is of non-homogeneous pixels. The classification done in each group is to identify those in the minority. Both proposed algorithms are very simple in computation and each of them is incorporated into the contrast enhancement procedure to make the integrated low-pass filters effectively remove the noise generated in the histogram equalization while well preserving the signal details. The simulation results demonstrates, by subjective observation and objective measurements, that the proposed algorithms lead to a superior quality of the contrast enhancement for varieties of images, with respect to two advanced enhancement schemes