Research On Image Enhancement Methods And Applications

Techniques of image enhancement aim at improving the interpretability or perception of information in images for human viewers, eliminating or attenuating unneeded information, or providing better input for other automated image processing techniques. The design of en-hancement methods closely relates to the aim of applications.In recent years, consumer and professional digital cameras are becoming more and more popular, hence enormous amount of image data are being generated. Visual effects of images taken in high dynamic range scenes, dim scenes, or under special lighting conditions are usually not satisfying. Hence, in order to fulfill the requirement for high quality display and print, post enhancement steps are necessary to expand or compress the luminance ratios, or to achieve color constancy. The human visual system excels in perception of scenes in different conditions. Image enhancement methods have been developed to describe the characteristics of human perception, in order to produce enhancement results similar to the human perceived scenes. Several image enhancement approaches are studied in the dissertation, and the advantages and disadvantages are analyzed. Some improvements to these approaches are also proposed. The work of the dissertation includes the following parts:(1) A fast Retinex image enhancement method that models both global and local adaptation of the human visual system is proposed to overcome the deficiency of halo artifacts which tradi-tional center/surround-based Retinex enhancement methods often suffer. Mean Shift filtering is performed on the original image in the illumination estimation step. Due to the capability of discontinuity preservation of adaptive filtering, halo artifacts can be effectively reduced by the proposed method. Experimental results demonstrate that the method can efficiently render high dynamic range images, and the results are compared with previous methods. An acceler-ated implementation of Mean Shift filtering is employed, thus the method is much faster than previous methods.(2) An improved version of Automatic Color Equalization (ACE) is proposed for the en-hancement of image contrast. A local filtering is performed first by taking account the spatial distribution of grayscale or color in the image, and an improved relative lightness appearance function is employed. Then the dynamic range of the image is adjusted for the final result. By keeping the proportion among the RGB channels, the color of the original image is pre-served in processing of color images. Experimental results demonstrate that the contrast can be effectively enhanced and no significant noise is introduced by this method.(3) Recently variational and partial differential equation-based methods have been widely used in the image processing community. An improved image enhancement method in the per-ceptually inspired variational framework is proposed. A spatial adaptive parameter determined by local image features is developed to regulate the contrast enhancement. The minimum of the functional is computed using a gradient descent approach. Experimental results demon-strate that the method can enhancement the contrast of color images and details in dark and flat regions can be effectively improved.(4) Common contrast enhancement methods compress the dynamic range of images while increasing contrast and enhancing the visibility of details in the darker regions. At the same time, details in the brighter regions are usually not enhanced, or even attenuated in some cases. The novelty of the proposed method is to take the human visual perceptual sensitivity into con-sideration. Relying on the observation that the perceived contrast is less at regions with higher local average luminance levels, the idea of relative gradients is introduced and a gradient do-main method that tends to enhance contrast in brighter regions more is proposed. Experimental results demonstrate that details in both brighter and darker regions of the original images are enhanced or preserved.(5) A fusion approach in the gradient domain to combine complementary advantages be-tween results by different image enhancement methods for visualization improvement is pro-posed. A weighted structure tensor is employed to capture significant details of each input channel, and local contrast is incorporated in the design of fusion weights. The target gradi-ent field is obtained from the structure tensor in the Frobenius norm sense. An energy term related to perceptual enhancement is incorporated into the fusion model. Experimental results demonstrate that the fused image can preserve significant detail and structural information of each input image, and the visual effect is improved. Several other applications of the proposed fusion method are presented as well.