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.

Object oriented fuzzy filter for noise reduction of Pgm images

Abstract: Noise is an important factor that influences image quality, which is mainly produced in the processes of image acquirement and transmission. Noise reduction is necessary for us to do image processing and image interpretation so as to acquire useful information that we want. This paper presents a novel approach to the problem of noise reduction for PGM images. The proposed technique is based on the maximization of the similarities between pixels in the filtering window. The method is able to remove the noise component, while adapting itself to the local image structure. In this way, the proposed algorithm eliminates impulsive noise while preserving edges and fine image details. Since the algorithm can be considered as a modification of the vector median filter driven by fuzzy membership functions, it is fast, computationally efficient, and easy to implement. Simulation results show that the proposed fuzzy filter effectively removes the noise by preserving image originality.

Integration of filters and phase unwrapping algorithms for removing noise in image reconstruction

Abstract: A method to detect and remove noise in image reconstruction. The method includes integration of filters and phase unwrapping algorithms for removing speckle noise, residual noise and noise at the lateral surface of height discontinuities. The method is used for generating a noise-free unwrapped phase map and hence, a successful image reconstruction of an object image.

Analytical Comparison of Noise Reduction Filters for Image Restoration Using SNR Estimation

Abstract: Noise removal from images is a part of image restoration in which we try to reconstruct or recover an image that has been degraded by using apriori knowledge of the degradation phenomenon. Noises present in images can be of various types with their characteristic Probability Distribution Functions (PDF). Noise removal techniques depend on the kind of noise present in the image rather than on the image itself. This paper explores the effects of applying noise reduction filters having similar properties on noisy images with emphasis on Signal-to-Noise-Ratio (SNR) value estimation for comparing the results.

Speckle noise reduction from ultrasound image using modified binning method and fuzzy inference system

Abstract: Ultrasound images contain Speckle Noise which degrades the quality of the images. Eliminating such noise is an important preprocessing task. This paper describes and analyses an algorithm for cleaning speckle noise in ultrasound medical images. In this method the presence of Speckle noise is detected by a simpler method called a Fuzzy Logic based Technique (FLT). However, the filtering idea is to recover the healthy pixel by the help of neighboring pixels. Sometimes the loss of edges or presence of noise makes the image noisy or blurred in appearance. To acquire a better performance we state an another method based on Binning Method that works efficiently to denoise an image without blurring the frontiers between different regions. To evaluate the performance we calculate the Signal to Noise Ratio, The Peak Signal to Noise Ratio, The Root Mean Square Error, The Edge Preservative Factor. This method gives the better performance than Existing Method [1].

Bit-depth extension in a digital display using quantization of noise-compensated image data

Abstract: A method for extending bit-depth of display systems. The method includes the steps of measuring the static display noise of a display device (14), using the display noise to create pseudo-random noise (12) and subtracting the pseudo-random noise (12) from a contone image (10). After the noise-compensated image data is quantized and displayed, the noise in the display device (14) will substantially convert the noise-compensated image data back to contone image data with few or no contouring artifacts. Other embodiments include using the inherent noise of the human visual system (22) instead of the static display noise, or both. Specific adjustments can be made to the noise of the human visual system (22) for color displays.