Multiscale Retinex with Color Restoration (MSRCR) algorithm has an extensive usage in many different applications and performs well for a very large variety of natural images. However, processing in three spectral channels for at least three distinct scale constants makes the algorithm time consuming. In addition, optimal performance is not always obtained with default parameter settings, especially when images that have a dark subject with a very bright background are being processed. In this paper, to overcome the drawbacks of the Retinex algorithm, a wavelet transform (WT) based Retinex approach is proposed, in which a modified version of the MSRCR algorithm is applied to the approximation coefficients of the transformed image. The detail coefficients are also changed in order to provide sharpness besides contrast enhancement. The proposed algorithm has less computational complexity, and also provides more realistic rendition compared to MSRCR.
Hyperspectral image (HSI) classification consists of a variety of algorithms involving supervised or unsupervised. In supervised classification, some reference data are used. Training data are not used in unsupervised classification methods. The type of a classification algorithm depends on the nature of the input and reference data.
The spectral matching, statistical and kernel based methods are the most widely known classification algorithms for hyperspectral imaging. Spectral matching algorithms try to identify the similarity of the unknown spectral signature of test pixels with the expected signature. Even though most spectra in real applications are random, the amount of training data with respect to the dimensionality affects the performances of the statistical classifiers substantially.
In this study, an efficient spectral similarity method employing Multi-Scale Vector Tunnel Algorithm (MS-VTA) for supervised classification of the materials in hyperspectral imagery is introduced. With the proposed algorithm, a simple spectral similarity based decision rule using limited amount of reference data or spectral signature is formed and compared with the Euclidian Distance (ED) and the Spectral Angle Map (SAM) classifiers. The prediction of multi-level upper and lower spectral boundaries of spectral signatures for all classes across spectral bands constitutes the basic principle of the proposed algorithm.
A novel spatial domain image enhancement algorithm, in which dynamic range of the scene illumination is compressed from the human visual perspective to improve the visual quality and visibility in digital images captured under degraded visual conditions, is proposed. The proposed algorithm employs an adaptive approach so that local image statics, namely the local standard deviation and the local mean in the image are modified simultaneously utilizing an intensity transform based on a “S” shape curve, the curvature parameter of which is determined adaptively followed by a local contrast enhancement process. The performance of the algorithm is evaluated by a statistical visual measure, along with visual comparisons of the proposed method with state-of-the-art enhancement algorithms are given.
Proc. SPIE. 7341, Visual Information Processing XVIII
KEYWORDS: Image compression, Visualization, Image processing, Wavelets, Image quality, Color image processing, High dynamic range imaging, Image enhancement, Image contrast enhancement, Real time video processing
Recently we proposed a wavelet-based dynamic range compression algorithm to improve the visual quality of digital
images captured from high dynamic range scenes with non-uniform lighting conditions. The fast image enhancement
algorithm that provides dynamic range compression, while preserving the local contrast and tonal rendition, is also a
good candidate for real time video processing applications. Although the colors of the enhanced images produced by
the proposed algorithm are consistent with the colors of the original image, the proposed algorithm fails to produce
color constant results for some "pathological" scenes that have very strong spectral characteristics in a single band.
The linear color restoration process is the main reason for this drawback. Hence, a different approach is required for
the final color restoration process. In this paper the latest version of the proposed algorithm, which deals with this issue
is presented. The results obtained by applying the algorithm to numerous natural images show strong robustness and
high image quality.
An adaptive technique for image enhancement based on a specifically designed nonlinear function is presented in this
paper. The enhancement technique constitutes three main processes-adaptive intensity enhancement, contrast
adjustment, and color restoration. A sine function with an image dependent parameter is used to tune the intensity of
each pixel in the luminance image. This process provides dynamic range compression by boosting the luminance of
darker pixels while reducing the intensity of brighter pixels and maintaining local contrast. The normalized reflectance
image is added to the enhanced image to preserve the details. The quality of the enhanced image is improved by applying
a local contrast enhancement followed by a contrast stretch process. A basic linear color restoration process based on the
chromatic information of the original image is employed to convert the enhanced intensity image back to a color image.
The performance of the algorithm is compared with other state of the art enhancement techniques and evaluated using a
statistical image quality evaluation method.
In this paper, a new wavelet-based dynamic range compression algorithm is proposed to improve the visual quality of
digital images captured in the high dynamic range scenes with non-uniform lighting conditions. Wavelet transform is
used especially for dimension reduction such that a dynamic range compression with local contrast enhancement
algorithm is applied only to the approximation coefficients which are obtained by low-pass filtering and down-sampling
the original intensity image. The normalized approximation coefficients are transformed using a hyperbolic sine curve
and the contrast enhancement is realized by tuning the magnitude of the each coefficient with respect to surrounding
coefficients. The transformed coefficients are then de-normalized to their original range. The detail coefficients are also
modified to prevent the edge deformation. The inverse wavelet transform is carried out resulting in a low dynamic range
and contrast enhanced intensity image. A color restoration process based on relationship between spectral bands and the
luminance of the original image is applied to convert the enhanced intensity image back to a color image.
A local statistics based contrast enhancement technique for enhancing the reconstructed high resolution image from a set of shifted and rotated low resolution images is proposed in this paper. Planar shifts and rotations in the low resolution images are determined by a phase correlation approach performed on the polar coordinate representations of their Fourier transforms. The pixels of the low resolution images are expressed in the coordinate frame of the reference image and the image values are interpolated on a regular high-resolution grid. The non-uniform interpolation technique which allows for the reconstruction of functions from samples taken at non-uniformly distributed locations has relatively low computational complexity. Since bi-cubic interpolation produces blurred edges due to its averaging effect, the edges of the reconstructed image are enhanced using a local statistics based approach. The center-surround ratio is adjusted using global statistics of the reconstructed image and used as an adaptive gamma correction to achieve the local contrast enhancement which increases the image sharpness. Performance of the proposed algorithm is evaluated by conducting experiments on both synthetic and real image sets and the results are encouraging in terms of visual quality.