A solution to the deficiencies of image enhancement
Signal Processing
No-reference image quality assessment in contourlet domain
Neurocomputing
Context-based defading of archive photographs
Journal on Image and Video Processing - Special issue on image and video processing for cultural heritage
EURASIP Journal on Advances in Signal Processing - Special issue on theory and application of general linear image processing
Human visual system for complexity reduction of image and video restoration
CAIP'11 Proceedings of the 14th international conference on Computer analysis of images and patterns - Volume Part II
Degradation of turbid images based on the adaptive logarithmic algorithm
Computers & Mathematics with Applications
The symmetric logarithmic image processing model
Digital Signal Processing
Automatic contrast enhancement of low-light images based on local statistics of wavelet coefficients
Digital Signal Processing
Contrast enhancement of fog and haze stereo images based on mobile computing
International Journal of Wireless and Mobile Computing
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Varying scene illumination poses many challenging problems for machine vision systems. One such issue is developing global enhancement methods that work effectively across the varying illumination. In this paper, we introduce two novel image enhancement algorithms: edge-preserving contrast enhancement, which is able to better preserve edge details while enhancing contrast in images with varying illumination, and a novel multihistogram equalization method which utilizes the human visual system (HVS) to segment the image, allowing a fast and efficient correction of nonuniform illumination. We then extend this HVS-based multihistogram equalization approach to create a general enhancement method that can utilize any combination of enhancement algorithms for an improved performance. Additionally, we propose new quantitative measures of image enhancement, called the logarithmic Michelson contrast measure (AME) and the logarithmic AME by entropy. Many image enhancement methods require selection of operating parameters, which are typically chosen using subjective methods, but these new measures allow for automated selection. We present experimental results for these methods and make a comparison against other leading algorithms.