Dark-frame subtraction

About: Dark-frame subtraction is a research topic. Over the lifetime, 1216 publications have been published within this topic receiving 20763 citations.

Gaussian noise image restoration using local statistics

Abstract: We propose an efficient noise estimation-based method in removing Gaussian noise using local statistics. A proposed detector flexibly classifies the serious and mild noisy pixels prior to applying the strong and weak filters respectively.

Analysis the Impact of Filters in Spatial Domain on Grayscale Image

Abstract: Computer graphics is the branch of computer science which deals with the study of graphics and images. Advancement in the technology brings the development in the image processing techniques, which deals with the image acquisition, image enhancement, restoration etc. Different type of noise got added up to the image while image acquisition leading to the final corrupted image. The main objective of this paper is to remove noise from the gray scale images to better understand the contents of the original image by applying various filter algorithms to get the blurred free and noise free image. For this an algorithm is developed and presented in shape of flowchart. This paper compares the different filters used for the noise removal from an image. It studies various filters for noise removal and finds the opinion that which is best for every type of image. As every image processing algorithms works differently for different image, there are different methods to deal with different types of noise.

Noise-robust image deblurring by blending regular- and short-exposure images

Abstract: We propose a new image deblurring method using differently exposed image pair. Regular-exposure image has more blur but less noise, while short-exposure image has more noise but less blur. Conventional approaches blend the two images using only good features of them based on the difference between the degradations. Although these approaches are effective under normal conditions, it is difficult to distinguish blur from noise under low light conditions. So we made two improvements to deal with large noise. One is using the gradient information of the regular-exposure image to refine the motion blur detection. The other is that noise on the edges is effectively suppressed considering the edge shapes and the noise levels on each pixel in the blended image. Finally, we implemented our method on the digital still camera and we successfully obtained the higher-quality images with less blur and noise through the simulations as well as the real camera examinations.

Impulse noise removal by using one-dimensional switching median filter applied along space-filling curve reflecting structural context of image

Abstract: A switching median filter (SMF) is effective for impulse noise removal while still preserving edges in an input image. This filter firstly detects pixels corrupted by impulse noise and then filters only the noise-corrupted pixels. However the noise detection process does not always work perfectly. Particularly, pixels constituting thin lines in an input image tend to be incorrectly detected as noise-corrupted pixels, and such pixels are filtered despite the needlessness of the filtering. As the result of the filtering, the image might be over-smoothed and be deteriorated throughout the entire image. To cope with this problem, we propose a new impulse noise removal method based on a one-dimensional SMF and a space-filling curve which reflects structural contexts of an input image. The effectiveness of the proposed method is verified by some experiments.

Mixed Noise Correction in Gray Images using Fuzzy Filters

Abstract: This paper presents Gaussian and Impulse noise filters for eliminating mixed noise in images. For Gaussian filter, the fuzzy set called “small” is derived to represent the disorder in a pixel arising out of neighborhood corrupted with Gaussian. The expression for correction is developed based on the intensity of the central pixel and the membership function. Similarly, the correction for the Impulse noise is developed by finding the middle ranking pixels in the neighborhood of the central pixel. The difference between the average of the middle ranking pixels and the central pixels is used to evaluate the membership function which when multiplied by the difference gives the correction. Consequently, the presence of noise is detected by finding the aggregate of the four highest memberships of the neighborhood pixels. If this aggregate is more than the threshold then there is Gaussian noise otherwise impulse noise. Accordingly, the corrupted pixel will be corrected by the correction term. The results are found to be satisfactory.