This paper proposes a Gaussian mixture based image enhancement method which uses particle swarm optimization
(PSO) to have an edge over other contemporary methods. The proposed method uses the guassian mixture model to
model the lightness histogram of the input image in CIEL*a*b* space. The intersection points of the guassian
components in the model are used to partition the lightness histogram. . The enhanced lightness image is generated by
transforming the lightness value in each interval to appropriate output interval according to the transformation function
that depends on PSO optimized parameters, weight and standard deviation of Gaussian component and cumulative
distribution of the input histogram interval. In addition, chroma compensation is applied to the resulting image to reduce
washout appearance. Experimental results show that the proposed method produces a better enhanced image compared to
the traditional methods. Moreover, the enhanced image is free from several side effects such as washout appearance,
information loss and gradation artifacts.
Digital still cameras generally use an optical low-pass filter(OLPF) to enhance the image quality by removing high spatial frequencies causing aliasing. While eliminating the OLPF can save manufacturing costs, images captured without using an OLPF include moiré in the high spatial frequency region of the image. Therefore, to reduce the presence of moiré in a captured image, this paper presents a moiré reduction method without the use of an OLPF. First, the spatial frequency response(SFR) of the camera is analyzed and moiré regions detected using patterns related to the SFR of the camera. Using these detected regions, the moiré components represented by the inflection point between the high frequency and DC components in the frequency domain are selected and then removed. Experimental results confirm that the proposed method can achieve moiré reduction while preserving detail information.
To acquire images in low-light environments, it is usually necessary to adopt long exposure times or to resort to
flashes. Flashes, however, often induce color distortion, cause the red-eye effect and can be disturbing to the subjects. On
the other hand, long-exposure shots are susceptible to subject-motion, as well as motion-blur due to camera shake when
performed with a hand-held camera. A recently introduced technique to overcome the limitations of the traditional lowlight
photography is the use of the multi-spectral flash. Multi-spectral flash images are a combination of UV/IR and
visible spectrum information. The general idea is to retrieve the details from the UV/IR spectrum and the color from the
visible spectrum. Multi-spectral flash images, however, are themselves subject to color distortion and noise. In this work,
a method of computing multi-spectral flash images so as to reduce the noise and to improve the color accuracy is
presented. The proposed method is a previously seen optimization method, improved by introducing a weight map used
to discriminate the uniform regions from the detail regions. The optimization target function takes into account the
output likelihood with respect to the ambient light image, the sparsity of image gradients, and the spectral constraints for
the IR-red and UV-blue channels. The performance of the proposed method was objectively evaluated using longexposure
shots as references.