A robust and efficient image enhancement technique has been developed to improve the visual quality of digital images that exhibit dark shadows due to the limited dynamic ranges of imaging and display devices which are incapable of handling high dynamic range scenes. The proposed technique processes images in two separate steps: dynamic range compression and local contrast enhancement. Dynamic range compression is a neighborhood dependent intensity transformation which is able to enhance the luminance in dark shadows while keeping the overall tonality consistent with that of the input image. The image visibility can be largely and properly improved without creating unnatural rendition in this manner. A neighborhood dependent local contrast enhancement method is used to enhance the images contrast following the dynamic range compression. Experimental results on the proposed image enhancement technique demonstrates strong capability to improve the performance of convolutional face finder compared to histogram equalization and multiscale Retinex with color restoration without compromising the false alarm rate.
A robust segmentation and boundary tracking technique for the extraction of the region and boundary of mammalian
cells in bioelectric images is presented. The proposed algorithm consists of four steps. The first step is an image
enhancement process composed of low-pass filtering and local contrast enhancement. The second step employs recursive
global adaptive thresholding method based on the statistical information of the contrast enhanced image to separate cells
and some other image features from the background. Due to the effective image enhancement produced in the previous
step, global adaptive thresholding is sufficient to provide satisfactory image thresholding results. The third step in the
segmentation process is composed of boundary tracking and morphological measurement for cell detection. A new
efficient boundary tracking scheme is proposed. In the last step non-cell objects are found and removed from the
segmented image based on the morphological information obtained in the last step.
An efficient real-time video stream enhancement algorithm based on illuminance-reflectance model is proposed for improving the visual quality of digital video streams captured under insufficient and/or non-uniform lighting conditions. The paper presents computational methods for estimation of scene illuminance and reflectance, adaptive dynamic range compression of illuminance, and adaptive enhancement for mid-tone frequency components. This algorithm is an effective and efficient technique for image enhancement with relatively simple computational procedures, which makes real-time enhancement of digital videos successfully realized. It also demonstrates strong robustness and high image quality when compared to other techniques.
A novel image enhancement algorithm called AINDANE (adaptive and integrated neighborhood dependent approach for nonlinear enhancement) for improving the visual quality of digital images captured under extremely low or nonuniform lighting conditions is proposed. AINDANE is comprised of two separate processes, namely, adaptive luminance enhancement and adaptive contrast enhancement, to provide more flexibility and better control over image enhancement. Adaptive luminance enhancement is a global intensity transformation based on a specifically designed nonlinear transfer function, which is self-tuned by the histogram statistics of the input image. This process largely increases the luminance of darker pixels and compresses the dynamic range of the image at the same time. Adaptive contrast enhancement tunes the intensity of each pixel based on its relative magnitude with respect to the neighboring pixels. This process is also adaptively controlled by the global statistics of the image. A color restoration process, based on the relationship between the spectral bands and the luminance of the original image, is applied to convert the enhanced intensity image back to a color image.