Channel (digital image)

About: Channel (digital image) is a research topic. Over the lifetime, 7211 publications have been published within this topic receiving 69974 citations.

Efficient underwater image and video enhancement based on Retinex

Abstract: The Retinex models the human visual system to perceive natural colors, which could improve the contrast and sharpness of the degraded image and also provide color constancy and dynamic range simultaneously. This endows the Retinex exceeding advantages for enhancing the underwater image. Based on the multi-scale Retinex, an efficient enhancement method for underwater image and video is presented in this paper. Firstly, the image is pre-corrected to equalize the pixel distribution and reduce the dominating color. Then, the classical multi-scale Retinex with intensity channel is applied to the pre-corrected images for further improving the contrast and the color. In addition, multi-down-sampling and infinite impulse response Gaussian filtering are adopted to increase processing speed. Subsequently, the image is restored from logarithmic domain and the illumination of the restored image is compensated based on statistical properties. Finally, the color is selectively preserved by the inverted gray world method depending on imaging conditions and application requirements. Five kinds of typical underwater images with green, blue, turbid, dark and colorful backgrounds and two underwater videos are enhanced and evaluated on Jetson TX2, respectively, to verify the effectiveness of the proposed method.

Low-light image enhancement using CNN and bright channel prior

Abstract: In this paper, we propose a joint framework to enhance images under low-light conditions. First, a convolutional neural network (CNN) based architecture is proposed to denoise low-light images. Then, based on atmosphere scattering model, we introduce a low-light model to enhance image contrast. In our low-light model, we propose a simple but effective image prior, bright channel prior, to estimate the transmission parameter; besides, an effective filter is designed to adaptively estimate environment light in different image areas. Experimental results demonstrate that our method achieves superior performance over other methods.

Real-time contrast preserving decolorization

Abstract: Decolorization -- the process to transform a color image to a grayscale one -- is a basic tool in digital printing, stylized black-and-white photography, and in many single channel image and video processing applications While recent research focuses on retaining meaningful visual features and color contrast, less attention has been paid to the complexity issue of the conversion Consequently, the resulting decolorization methods could be orders of magnitude slower than simple procedures, eg, Matlab built-in rgb2gray function, which could hamper them from being used practically In this paper, we propose a very fast and yet effective decolorization approach The effectiveness of the method is borne out by a new quantitative metric as well as qualitative comparisons with state-of-the-art methods

Single image haze removal considering sensor blur and noise

Abstract: Images of outdoor scenes are usually degraded under bad weather conditions, which results in a hazy image. To date, most haze removal methods based on a single image have ignored the effects of sensor blur and noise. Therefore, in this paper, a three-stage algorithm for haze removal, considering sensor blur and noise, is proposed. In the first stage, we preprocess the degraded image and eliminate the blur/noise interference to estimate the hazy image. In the second stage, we estimate the transmission and atmospheric light by the dark channel prior method. In the third stage, a regularized method is proposed to recover the underlying image. Experimental results with both simulated and real data demonstrate that the proposed algorithm is effective, based on both the visual effect and quantitative assessment.