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That the amount of the clarity in the color attribute in the single-image depends on the intensity of illumination, as well as the type of light used and the angle of its fall.This research we adopted in the light of the sun and different intensities on throughout the day and the angle of light on the picture, the purpose of Study changes in the color information received from camera. The proposed methods to address these data to get the clearest picture, It was also we working to shed different wavelengths between 400 to 750 nm and the wavelength of 255 nm and 366 nm for the purpose of studying the characteristics of the image. And we can looking for the disappearance of some band of color (RGB) colorimetric with small Concentration and its appearance at other times due to changes in the angle and intensity of light on the image, so the color shows the characteristic change color in the picture and the emergence of a difference in the captured image with time. The adoption of the mediator and the rate calculated the amount of the standard deviation of the color band in all the captured images. In order to find out which color packets disappear or appear based on light intensity, It was also we deducted three areas of each color of the primary colors in the image ( red, Green, blue) and calculate the amount of the standard deviation for it to know the times that appear or disappear some of the color packets. General Terms The general terms we used general classification of image analysis by different time taken image for get better depend on the sources light.

method produces a clean, ghost-free image with fine details. More importantly, our method is significantly faster than other methods. This paper presents a fast denoising method that produces a clean image from a burst of noisy images. We accelerate alignment of the images by introducing a lightweight camera motion representation called homography flow. The aligned images are then fused to cre-ate a denoised output with rapid per-pixel operations in temporal and spatial domains. To handle scene motion during the capture, a mechanism of selecting consistent pixels for temporal fusion is pro-posed to “synthesize ” a clean, ghost-free image, which can largely reduce the computation of tracking motion between frames. Com-bined with these efficient solutions, our method runs several orders of magnitude faster than previous work, while the denoising qual-ity is comparable. A smartphone prototype demonstrates that our method is practical and works well on a large variety of real exam-ples.

permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Denoising is an indispensable function for digital cameras. In respect that noise is diffused during the demosaicking, the denoising ought to work directly on bayer data. The difficulty of denoising on bayer image is the interlaced mosaic pattern of red, green, and blue. Guided filter is a novel time efficient explicit filter kernel which can incorporate additional information from the guidance image, but it is still not applied for bayer image. In this work, we observe that the green channel of bayer mode is higher in both sampling rate and Signal-to-Noise Ratio (SNR) than the red and blue ones. Therefore the green channel can be used to guide denoising. This kind of guidance integrates the different color channels together. Experiments on both actual and simulated bayer images indicate that green channel acts well as the guidance signal, and the proposed method is competitive with other popular filter kernel denoising methods. 1.

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Copyright © 2014 Y. Kang and W. Pan.This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Illumination variation makes automatic face recognition a challenging task, especially in low light environments. A very simple and efficient novel low-light image denoising of low frequency noise (DeLFN) is proposed. The noise frequency distribution of low-light images is presented based on massive experimental results. The low and very low frequency noise are dominant in low light conditions. DeLFN is a three-level image denoising method. The first level denoises mixed noises by histogram equalization (HE) to improve overall contrast. The second level denoises low frequency noise by logarithmic transformation (LOG) to enhance the image detail. The third level denoises residual very low frequency noise by high-pass filtering to recover more features of the true images. The PCA (Principal Component Analysis) recognition method is applied to test recognition rate of the preprocessed face images with DeLFN. DeLFN are compared with several representative illumination preprocessing methods on the Yale Face Database B, the Extended Yale face database B, and the CMU PIE face database, respectively. DeLFN not only outperformed other algorithms in improving visual quality and face recognition rate, but also is simpler and computationally efficient for real time applications. 1.