In the current optical remote sensing field, it has continuously evolved into a multi-layered and multi-perspective observation system. Faced with the complexities of observation tasks, diverse observation methods, and the refinement of observation targets, there is a need for more in-depth research on denoising of remote sensing images. Traditional denoising algorithms often produce denoised images with overly smooth edge textures, leading to the loss of small targets within the images. Therefore, addressing the aforementioned issues, this paper proposes an improved denoising algorithm based on the Transformer network structure. This algorithm employs attention operations across channel dimensions and utilizes feature recalibration. This allows the model to determine the importance of various feature channels, thereby avoiding the significant computational overhead brought about by the traditional Transformer's self-attention enhancement in spatial dimensions. Moreover, the algorithm utilizes a U-shaped denoising module, which effectively reduces the semantic gap between image feature mappings, resulting in the restoration of better image features. The experiments indicate that when tested on remote sensing image datasets, the proposed algorithm outperforms current representative algorithms in both subjective and objective evaluation metrics. While effectively removing image noise, it also better preserves edge details and texture features, achieving superior visual results.
KEYWORDS: Remote sensing, Cameras, Interference (communication), Data modeling, Denoising, Dark current, Signal to noise ratio, Process modeling, Convolution, Image processing
With the continuous development of the field of remote sensing, the field of space-based remote sensing is developing in the direction of all-sky time and intelligence. Since low-light remote sensing is used to detect ground objects under low illumination conditions such as night and dawn and dusk, it leads to the characteristics of low contrast, low brightness and low signal-to-noise ratio of remote sensing images. The low signal-to-noise ratio leads to a large number of complex physical noises which will drown the image features and seriously affect the recognition and interpretation of ground objects. In this paper, a real physical model construction method based on physical mechanism of low-light image is proposed, By solving the noise parameters of space-based low-light remote sensing camera, the specific noise distribution model is constructed, which is used to synthesize the training data of the training denoising network, Thus, it gets rid of the difficult and laborious calibration problem caused by the lack of space-based data. In addition, in order to simulate the real imaging scene of space-based remote sensing camera, a set of low-light remote sensing noise image data set based on real physical model is constructed for the first time, which effectively promotes the development of subsequent image denoising research.
Target acquisition task-oriented effectiveness evaluation methods for optical remote sensing systems are helpful in the design phase to quantitatively evaluate and predict the target detection ability of optical remote sensing systems. Compared with the widely-used National Imagery Interpretability Rating Scale (NIIRS) and Johnson criteria, the Targeting Task Performance (TTP) metric has a more comprehensive evaluation model for electro-optical imaging systems, combined with the target features and human eye functions. This paper discussed the effects of the image luminance and noise distribution on the target acquisition task, and proposed a revised TTP metric, aiming at improving the accuracy of the effectiveness evaluation model. Aerial remote sensing experiments were carried out, and the ability of an optical remote sensing system to detect and recognize the target models was evaluated based on the proposed method. For aerial images under the NIIRS levels ranged from 2.98 to 4.43. The detection and recognition probabilities of aircraft models were calculated using the original and revised TTP metrics, respectively. It is inferred that the evaluation results by the revised TTP metric are in better accordance with the NIIRS level of the images.
The low-light-level sensing technology has been subject to the development of image sensors. The later, developed in the recent decades, has been improved from the low light level image intensifier, EBCCD, to recently, the CMOS technology. New technologies, for instance, APS(Active Pixel Sensor), back illuminated, etc. have been supplied following the CMOS process technology, resulting in a series of CMOS detectors with high sensitivity, lower noise and fewer operation restrictions. However, as a weak signal detection system, the operation condition, for example, the temperature characteristics related with the exposure time, ambient brightness, and target brightness, also cannot be ignored. In this paper, we presented a detailed temperature characteristic analysis based on a low-light-level CMOS system. The dark current was verified based on all black tests. The calculation as followed was drawn based on the varying ambient conditions and settings of the system:1, A typical dark current curve was obtained from the experiment, double every 9℃ with environment temperature increased.2, the information acquisition ability is affected by the exposure time, ambient brightness, and target brightness, reflected by the dark current and photon noise. Meanwhile, it was proofed in this paper, in a low light level system, the traditional signal to noise ratio calculation could not delaminate the brightness caused by the dominate photo noise, resulting an error strong enough to affect signal. Therefore, in this paper, a SNR evaluate method which could improve the objectivity of the evaluation of the low light level image was provided. It was believed to be helpful to the future research.
Hyperspectral imaging is used to collect tens or hundreds of images continuously divided across electromagnetic spectrum so that the details under different wavelengths could be represented. A popular hyperspectral imaging methods uses a tunable optical band-pass filter settled in front of the focal plane to acquire images of different wavelengths. In order to alleviate the influence of chromatic aberration in some segments in a hyperspectral series, in this paper, a hyperspectral optimizing method uses sub-pixel MTF to evaluate image blurring quality was provided. This method acquired the edge feature in the target window by means of the line spread function (LSF) to calculate the reliable position of the edge feature, then the evaluation grid in each line was interpolated by the real pixel value based on its relative position to the optimal edge and the sub-pixel MTF was used to analyze the image in frequency domain, by which MTF calculation dimension was increased. The sub-pixel MTF evaluation was reliable, since no image rotation and pixel value estimation was needed, and no artificial information was introduced. With theoretical analysis, the method proposed in this paper is reliable and efficient when evaluation the common images with edges of small tilt angle in real scene. It also provided a direction for the following hyperspectral image blurring evaluation and the real-time focal plane adjustment in real time in related imaging system.
In the modern optical measurement field, the radius of curvature (ROC) is one of the fundamental parameters of optical lens. Its measurement accuracy directly affects the other optical parameters, such as focal length, aberration and so on, which significantly affect the overall performance of the optical system. To meet the demand of measurement instruments for radius of curvature (ROC) with high accuracy in the market, we develop a laser confocal radius measurement system with grating ruler. The system uses the peak point of the confocal intensity curve to precisely identify the cat-eye and confocal positions and then measure the distance between these two positions by using the grating ruler, thereby achieving the high-precision measurement for the ROC. The system has advantages of high focusing sensitivity and anti-environment disturbance ability. And the preliminary theoretical analysis and experiments show that the measuring repeatability can be up to 0.8 um, which can provide an effective way for the accurate measurement of ROC.
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