Bilateral filter

About: Bilateral filter is a research topic. Over the lifetime, 3500 publications have been published within this topic receiving 75582 citations.

Video denoising based on a spatiotemporal Kalman-bilateral mixture model.

Abstract: We propose a video denoising method based on a spatiotemporal Kalman-bilateral mixture model to reduce the noise in video sequences that are captured with low light. To take full advantage of the strong spatiotemporal correlations of neighboring frames, motion estimation is first performed on video frames consisting of previously denoised frames and the current noisy frame by using block-matching method. Then, current noisy frame is processed in temporal domain and spatial domain by using Kalman filter and bilateral filter, respectively. Finally, by weighting the denoised frames from Kalman filtering and bilateral filtering, we can obtain a satisfactory result. Experimental results show that the performance of our proposed method is competitive when compared with state-of-the-art video denoising algorithms based on both peak signal-to-noise-ratio and structural similarity evaluations.

A Novel Bio-inspired Algorithm for Color Image Enhancement

Abstract: We propose a novel bio-inspired algorithm to enhance the color image under low or non-uniform lighting conditions that models global and local adaptation of the human visual system.The proposed method consists of three parts:a preliminary global luminance adjustment followed by local contrast enhancement and color restoration.The global luminance adjustment increases the luminance of darker pixels and compresses the dynamic range as well.The local contrast enhancement adjusts the intensity of each pixel based on its relative magnitude with respect to the bilateral filter output of its neighboring pixels.Then a linear color restoration process is applied to convert the enhanced intensity image back to a color image.Experimental results of the proposed method and reference are compared and analyzed to illustrate the effectiveness of the proposed method.

Underwater image de-scattering and enhancing using dehazenet and hwd

Abstract: De-scattering and edge enhancing are critical procedures for underwater images which suffer from serious contrast attenuation, color deviation, and edge blurring. In this paper, a novel method is proposed to enhance underwater images. Firstly, a Convolutional Neural Network (CNN) is trained end-to-end to estimate the transmission map. Simultaneously, the adaptive bilateral filter is used to refine the transmission map. Secondly, a strategy based on the white balance is proposed to remove the color deviation. Laplace pyramid fusion is utilized to obtain the fusion result of the haze-free and color-corrected image. Finally, the output image is transformed into the Hybrid Wavelets and Directional Filter Banks (HWD) domain for de-noising and edge enhancing. The experimental results show that the proposed method can remove color distortion and improve the clarity of the underwater images. Objective and subjective results demonstrate that the proposed method outperforms several state-of-the- art methods in different circumstances.

Adaptive sharpening of photos

Abstract: Sharpness is an important attribute that contributes to the overall impression of printed photo quality. Often it is impossible to estimate sharpness prior to printing. Sometimes it is a complex task for a consumer to obtain accurate sharpening results by editing a photo on a computer. The novel method of adaptive sharpening aimed for photo printers is proposed. Our approach includes 3 key techniques: sharpness level estimation, local tone mapping and boosting of local contrast. Non-reference automatic sharpness level estimation is based on analysis of variations of edges histograms, where edges are produced by high-pass filters with various kernel sizes, array of integrals of logarithm of edges histograms characterizes photo sharpness, machine learning is applied to choose optimal parameters for given printing size and resolution. Local tone mapping with ordering is applied to decrease edge transition slope length without noticeable artifacts and with some noise suppression. Unsharp mask via bilateral filter is applied for boosting of local contrast. This stage does not produce strong halo artifact which is typical for the traditional unsharp mask filter. The quality of proposed approach is evaluated by surveying observer's opinions. According to obtained replies the proposed method enhances the majority of photos.

Universal strategy for surveillance video defogging

Abstract: We present a new approach to remove haze from surveillance video sequences. This approach extracts the background image through the frame differential method, uses the dark channel prior to estimate the atmospheric light, and then calculates a universal transmission map on the intensity component of the background image through a process of multiscale retinex, parameter adjustment, bilateral filtering, and total variation denoising filtering. Finally, it renders haze-free video according to the haze image model. The main advantage of the proposed approach is its speed as this approach adopts a "universal strategy" that applies the same atmospheric light and a universal pseudo-transmission map to a series of video frames. Experiments demonstrate that our method produces visually pleasing defogging results and tends to preserve main details better than previous techniques. A comparative study and quantitative evaluation show the efficiency of the proposed method.