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.

No-reference image quality assessment for removal of processed and unprocessed noise

Abstract: We present a fast no-reference quality measurement method based on the entropy analysis of the image structure. The information that an images carries is represented not only by intensity values but also their position in the image. We examine the behavior of the entropy by altering the positions to distinguish the image structure, noise and blur. We propose a method to rearrange the pixel placements using a random pixel scattering transform. Using this transform we firstly determine the local power of image structure relative to noise and then we obtain the quality index by combining this information with our proposed blur factor. We show that this method is useful to select denoising parameters automatically in both unprocessed (white Gaussian) and processed (frequency-dependent) noise when the reference image is not available. Our method is easy to implement and yet rivals state-of-the-art quality measurement approaches.

Evolving Image Noise Filters through Genetic Programming

Abstract: A form of Evolutionary Computation (EC) called Genetic Programming (GP) was used to automatically discover sequences of image noise filters to remove two types of image noise and a type of communications noise associated with a remotely sensed imagery. Sensor noise was modeled by the addition of salt-and-pepper and grayscale noise to the image. Communication noise was modeled by inserting a series of blank pixels in selected image rows to replicate dropped pixel segments occurring during communication interruptions of sequential uncompressed image information. A known image was used for training the evolver. Heavy amounts of noise were added to the known image, and a filter was evolved. (The filtered image was compared to the original with the average image-toimage pixel error establishing the fitness function.). The evolved filter derived for the noisy image was then applied to never-before-seen imagery affected by similar noise conditions to judge the universal applicability of the evolved GP filter. Examples of all described images are included in the presentation. A variety of image filter primitives were used in this experiment. The evolved sequences of primitives were each then sequentially applied to produce the final filtered image. These filters were evolved over a typical run length of one week each on a small Linux cluster. Once evolved, the filters were then transported to a PC for application to the never-before-seen images, using an “evolve-once, apply-many-times” approach. The results of this image filtering experiment were quite dramatic.

Filtering Noise on two dimensional image Using Fuzzy Logic Technique

Abstract: 1 Abstract This paper presents one simple and novel technique for removal of impulse noise from corrupted image data The algorithm involves impulse detection followed by spatial filtering of the corrupted pixels In this method the presence of impulse 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 This fuzzy logic filter is presented through 5 stages (1)A sliding moving window is constructed to check every pixel of the whole image(2)Two conditional rule are applied according to the averaging value of the neighboring pixels (3)some membership functions are generated to improve the intensity of pixel value so that it can be distinguishable(4) A simpler function is developed for better reorganization and removal of noisy data (5)The resulted matrix appears with suppression of less no of noise It is shown that FLT is a preferred method of rejecting impulse noise both in terms of computational complexity and lower residual NSR(noise to signal ratio)

Reduction of Noise in Speech Signals through Image Processing using the Spectrogram

Abstract: Speech recognition systems have come to be used widely. When any speech recognition system is disturbed by surrounding noises, considerable reduction in the recognition rate is inevitable. It is much desired to develop noise reduction methods so that any speech recognition system can be used in realistic environments. We propose a novel scheme especially effective for reducing the noise generated in the vehicles. The reduction is achieved through image processing techniques applied to the corresponding spectrograms. Experiments have been conducted on speech sounds in the vehicles. The performances have been evaluated in terms of the output signal-to-noise ratio (SNR). The proposed scheme has been compared with the conventional spectral subtraction method, and found to be promising especially for speeches corrupted with great amount of car noises.

Moving object detection method

Abstract: When correction values are respectively determined for noise components of “OFFSET COMPONENT OF CCD ELEMENT”, “GRADATION COMPONENT OF BACKGROUND LIGHT” and “OFFSET COMPONENT OF OPTICAL SYSTEM”, the pixel values including as less of these noise components as possible are evaluated. The evaluated pixel values include a noise component of “THERMAL NOISE PLUS READOUT NOISE COMPONENT” which is superposed onto the pixel values. With this taken into consideration, a moving object detection method of an embodiment photographs multiple images of a moving object being an observation object with a photographic area fixed, selects the smallest pixel value in each group of corresponding pixels across the images from image signals representing the images, evaluates image signals including as less of the four noise components as possible by using the smallest pixel value as the correction value for the four noise components.