Search Results (19)

Low-light image enhancement remains a challenging task in computer vision due to the complex interplay of noise, asymmetrical artifacts, illumination non-uniformity, and detail preservation. Existing methods such as traditional histogram equalization, gamma correction, and Retinex-based approaches often struggle to balance contrast improvement and naturalness preservation. Deep learning methods such as CNNs and transformers have shown promise, but face limitations in modeling multi-scale illumination and long-range dependencies. To address these issues, we propose WIGformer, a novel wavelet-based illumination-guided transformer framework for low-light image enhancement. The proposed method extends the single-stage Retinex theory to explicitly model noise in both reflectance and illumination components. It introduces a wavelet illumination estimator with a Wavelet Feature Enhancement Convolution (WFEConv) module to capture multi-scale illumination features and an illumination feature-guided corruption restorer with an Illumination-Guided Enhanced Multihead Self-Attention (IGEMSA) mechanism. WIGformer leverages the symmetry properties of wavelet transforms to achieve multi-scale illumination estimation, ensuring balanced feature extraction across different frequency bands. The IGEMSA mechanism integrates adaptive feature refinement and illumination guidance to suppress noise and artifacts while preserving fine details. The same mechanism allows us to further exploit symmetrical dependencies between illumination and reflectance components, enabling robust and natural enhancement of low-light images. Extensive experiments on the LOL-V1, LOL-V2-Real, and LOL-V2-Synthetic datasets demonstrate that WIGformer achieves state-of-the-art performance and outperforms existing methods, with PSNR improvements of up to 26.12 dB and an SSIM score of 0.935. The qualitative results demonstrate WIGformer’s superior capability to not only restore natural illumination but also maintain structural symmetry in challenging conditions, preserving balanced luminance distributions and geometric regularities that are characteristic of properly exposed natural scenes. Full article

The inhomogeneity of spectral pixel response is an unavoidable phenomenon in hyperspectral imaging, which is mainly manifested by the existence of inhomogeneity banding noise in the acquired hyperspectral data. It must be carried out to get rid of this type of striped noise since it is frequently uneven and densely distributed, which negatively impacts data processing and application. By analyzing the source of the instrument noise, this work first created a novel non-uniform noise removal method for a spatial dimensional push sweep hyperspectral imaging system. Clean and clear medical hyperspectral brain tumor tissue images were generated by combining scene-based and reference-based non-uniformity correction denoising algorithms, providing a strong basis for further diagnosis and classification. The precise procedure entails gathering the reference dark background image for rectification and the actual medical hyperspectral brain tumor image. The original hyperspectral brain tumor image is then smoothed using a weighted least squares algorithm model embedded with bilateral filtering (BLF-WLS), followed by a calculation and separation of the instrument fixed-mode fringe noise component from the acquired reference dark background image. The purpose of eliminating non-uniform fringe noise is achieved. In comparison to other common image denoising methods, the evaluation is based on the subjective effect and unreferenced image denoising evaluation indices. The approach discussed in this paper, according to the experiments, produces the best results in terms of the subjective effect and unreferenced image denoising evaluation indices (MICV and MNR). The image processed by this method has almost no residual non-uniform noise, the image is clear, and the best visual effect is achieved. It can be concluded that different denoising methods designed for different noises have better denoising effects on hyperspectral images. The non-uniformity denoising method designed in this paper based on a spatial dimension push-sweep hyperspectral imaging system can be widely used. Full article

In this paper, principal component analysis is introduced to form a scene-based nonuniformity correction method for infrared focal plane arrays. The estimation of the gain and offset of the infrared detector and the correction of nonuniformity based on the neural network method with a novel estimation of desired target value are achieved concurrently. The current frame and several adjacent registered frames are decomposed onto a set of principal components, and then the first principal component is extracted to construct the desired target value. It is practical, forms fewer ghosting artifacts, and considerably promotes correction precision. Numerical experiments demonstrate that the proposed method presents excellent performance when dealing with clean infrared data with synthetic pattern noise as well as the real infrared video sequence. Full article

To address the issues of fuzzy scene details, reduced definition, and poor visibility in images captured under non-uniform lighting conditions, this paper presents an algorithm for effectively enhancing such images. Firstly, an adaptive color balance method is employed to address the color differences in low-light images, ensuring a more uniform color distribution and yielding a low-light image with improved color consistency. Subsequently, the image obtained is transformed from the RGB space to the HSV space, wherein the multi-scale Gaussian function is utilized in conjunction with the Retinex theory to accurately extract the lighting components and reflection components. To further enhance the image quality, the lighting components are categorized into high-light areas and low-light areas based on their pixel mean values. The low-light areas undergo improvement through an enhanced adaptive gamma correction algorithm, while the high-light areas are enhanced using the Weber–Fechner law for optimal results. Then, each block area of the image is weighted and fused, leading to its conversion back to the RGB space. And a multi-scale detail enhancement algorithm is utilized to further enhance image details. Through comprehensive experiments comparing various methods based on subjective visual perception and objective quality metrics, the algorithm proposed in this paper convincingly demonstrates its ability to effectively enhance the brightness of non-uniformly illuminated areas. Moreover, the algorithm successfully retains details in high-light regions while minimizing the impact of non-uniform illumination on the overall image quality. Full article

Non-uniformity in the response of spectral image elements is an inevitable phenomenon in hyperspectral imaging, which mainly manifests itself as the presence of band noise in the acquired hyperspectral data. This problem is prominent in the infrared band owing to the detector material, operating environment, and other factors. Non-uniformity is an important factor that can affect the quality of the hyperspectral data, which has a serious impact on both data analysis and applications and requires corrections via technical means wherever possible. This paper proposes a novel target-based non-uniformity self-correction method for infrared push-broom hyperspectral images. The Mars Mineralogical Spectrometer (MMS) onboard the Tianwen-1 orbiter was used as the research and application object. The model is constructed and applied to the target scene characteristics and detection patterns of Mars remote sensing exploration, which are combined with the causes of noise generation in the infrared spectral image bands. The design of the MMS dual-channel Visible-Near-Infrared (V-NIR) and Near-Mid-Infrared (N-MIR) co-field of view co-target detection and laboratory calibration data for the V-NIR spectral band can achieve non-uniformity corrections (NUCs). Therefore, for the MMS in-orbit Mars exploration mission, the method selected spectral data (920–1055 nm) characterized by a reduced atmospheric influence to iteratively obtain the homogeneous region, which was used to calculate the non-uniformity correction factor for the N-MIR spectral band. This method was compared, validated, and evaluated with other conventional methods using both laboratory and in-orbit hyperspectral data. The results showed that the experimental data corrections were comparable to laboratory calibrations, with a maximum relative deviation of <2.6%. These results prove that our method not only provides an excellent non-uniformity correction, but also ensures spectral fidelity. It can thus be used as a non-uniformity correction process for the MMS and similar hyperspectral imagers. Full article

Non-uniformity commonly exists in the infrared focal plane, which behaves as the fixed-pattern noise (FPN) and seriously affects the image quality of long-wave infrared (LWIR) detection systems. The two-point correction (TPC) method is commonly used to reduce image FPN in engineering. However, when a wide-spectrum LWIR detection system calibrated with a black body is used to detect weak and small targets in the sky, FPN still appears in the image, affecting its uniformity. The effects of atmospheric transmittance characteristics of long-range paths on the non-uniformity of wide-spectrum long-wave infrared systems have not been studied. This paper proposes a modified TPC model based on spectral subdivision that introduces atmospheric transmittance. Additionally, the effects of atmospheric transmittance characteristics on the long-wave infrared non-uniform correction coefficient are analyzed. The experimental results for a black body scene and sky scene using a weak and small target detection system with a long-wave Sofradir FPA demonstrate that the wide-spectrum LWIR detection system fully considers atmospheric transmittance when performing calibration based on the TPC method, which can reduce the non-uniformity of the image. Full article

Uncooled thermal cameras have been employed as common UAV payloads for aerial temperature surveillance in recent years. Due to the lack of internal cooling systems, such cameras often suffer from thermal-drift-induced nonuniformity or vignetting despite having built-in mechanisms to minimize the noise. The current study examined a UAV-based uncooled thermal camera vignetting regarding camera warmup time, ambient temperature, and wind speed and direction, and proposed a simple calibration-based vignetting migration method. The experiments suggested that the camera needed to undergo a warmup period to achieve stabilized performance. The required warmup duration ranged from 20 to 40 min depending on ambient temperature. Camera vignetting severity increased with camera warmup time, decreasing ambient temperature, and wind presence, while wind speed and direction did not make a difference to camera vignetting during the experiments. Utilizing a single image of a customized calibration target, we were able to mitigate vignetting of outdoor images captured in a 30 min duration by approximately 70% to 80% in terms of the intra-image pixel standard deviation (IISD) and 75% in terms of the pixel-wise mean (PWMN) range. The results indicated that outdoor environmental conditions such as air temperature and wind speed during short UAV flights might only minimally influence the thermal camera vignetting severity and pattern. Nonetheless, frequent external shutter-based corrections and considering the camera nonlinear temperature response in future studies have the potential to further improve vignetting correction efficacy for large scene temperature ranges. Full article

Source-camera identification tools assist image forensics investigators to associate an image with a camera. The Photo Response Non-Uniformity (PRNU) noise pattern caused by sensor imperfections has been proven to be an effective way to identify the source camera. However, the PRNU is susceptible to camera settings, scene details, image processing operations (e.g., simple low-pass filtering or JPEG compression), and counter-forensic attacks. A forensic investigator unaware of malicious counter-forensic attacks or incidental image manipulation is at risk of being misled. The spatial synchronization requirement during the matching of two PRNUs also represents a major limitation of the PRNU. To address the PRNU’s fragility issue, in recent years, deep learning-based data-driven approaches have been developed to identify source-camera models. However, the source information learned by existing deep learning models is not able to distinguish individual cameras of the same model. In light of the vulnerabilities of the PRNU fingerprint and data-driven techniques, in this paper, we bring to light the existence of a new robust data-driven device-specific fingerprint in digital images that is capable of identifying individual cameras of the same model in practical forensic scenarios. We discover that the new device fingerprint is location-independent, stochastic, and globally available, which resolves the spatial synchronization issue. Unlike the PRNU, which resides in the high-frequency band, the new device fingerprint is extracted from the low- and mid-frequency bands, which resolves the fragility issue that the PRNU is unable to contend with. Our experiments on various datasets also demonstrate that the new fingerprint is highly resilient to image manipulations such as rotation, gamma correction, and aggressive JPEG compression. Full article

Infrared detectors suffer from severe non-uniform noise which highly reduces image resolution and point target signal-to-noise ratio. This is the restriction for airborne point target detection systems in reaching the background limit. The existing methods are either not accurate enough, or too complex to be applied to engineering. To improve the precision and reduce the algorithm complexity of scene-based Non-Uniformity Correction (NUC) for an airborne point target detection system, a Median-Ratio Scene-based NUC (MRSBNUC) method is proposed. The method is based on the assumption that the median value of neighboring pixels is approximately constant. The NUC coefficients are calculated recursively by selecting the median ratio of adjacent pixels. Several experiments were designed and conducted. For both the clear sky scene and scene with clouds, the non-uniformity is effectively reduced. Furthermore, targets were detected in outfield experiments. For Target 1 48.36 km away and Target 2 50.53 km away, employing MRSBNUC the SNR of the target increased 2.09 and 1.73 times respectively compared to Two-Point NUC. It was concluded that the MRSBNUC method can reduce the non-uniformity of the detector effectively which leads to a longer detection distance and fewer false alarms of the airborne point target detection system. Full article

The non-uniform response in infrared focal plane array (IRFPA) detectors inevitably produces corrupted images with a fixed-pattern noise. In this paper, we present a novel and adaptive scene-based non-uniformity correction (NUC) method called Correction method with Statistical scene-based and Interframe Registration (CSIR), which realizes low delay calculation of correction coefficient for infrared image. This method combines the statistical method and registration method to achieve a better NUC performance. Specifically, CSIR estimates the gain coefficient with statistical method to give registration method an appropriate initial value. This combination method not only reduces the need of interactive pictures, which means lower time delay, but also achieves better performance compared to the statistical method and other single registration methods. To verify this, real non-uniformity infrared image sequences collected by ourselves were used, and the advantage of CSIR was compared thoroughly on frame number (corresponding to delay time) and accuracy. The results show that the proposed method could achieve a significantly fast and reliable fixed-pattern noise reduction with the effective gain and offset. Full article

In this work, we introduce a temporal-spatial approach for infrared focal plane array (IRFPA) stripe nonuniformity correction in infrared images that generates visually appealing results. We posit that the nonuniformity appears as a striped structure in the spatial domain and that the pixel values change slowly in the temporal domain. Based on this, we formulate our correction method in two steps. In the first step, weighted guided image filtering with our adaptive weight is utilized to predict the stripe nonuniformity using a single frame. In the second step, the temporal profile of each pixel can be formed using a few frames of successive nonuniformity images. Further, we present a temporal nonlinear diffusion equation to remove scene residuals from the temporal profile of nonuniformity images in order to estimate a more accurate value of the stripe nonuniformity. The results of extensive experiments demonstrate that the proposed nonuniformity correction algorithm substantially outperforms many state-of-the-art approaches, including both traditional and deep convolution-neural-network-based methods, on four popular infrared videos. In addition, the proposed method only requires a fraction (less than ten) of the video frames. Full article

In this paper, the non-ideal factors, which include spatial noise and temporal noise, are analyzed and suppressed in the high-speed spike-based image sensor, which combines the high-speed scanning sequential format with the method that uses the interspike time interval to indicate the scene information. In this imager, spatial noise contains device mismatch, which results in photo response non-uniformity (PRNU) and the non-uniformity of dark current. By multiplying the measured coefficient matrix the photo response non-uniformity is suppressed, and the non-uniformity of dark current is suppressed by correcting the interspike time interval based on the time interval of dark current. The temporal noise is composed of the shot noise and thermal noise. This kind of noise can be eliminated when using the spike frequency to restore the image. The experimental results show that, based on the spike frequency method, the standard deviation of the image decreases from 18.4792 to 0.5683 in the uniform bright light by using the calibration algorithm. While in the relatively uniform dark condition, the standard deviation decreases from 1.5812 to 0.4516. Based on interspike time interval method, because of time mismatch and temporal noise, the standard deviation of the image changes from 27.4252 to 27.4977 in the uniform bright light by using the calibration algorithm. While in the uniform dark condition, the standard deviation decreases from 2.361 to 0.3678. Full article

Optical focal plane assemblies are increasingly being used in high-resolution optical satellite systems to enhance the width of the image using linear push-broom imaging. With this system, vignetting occurs in the area of overlap, affecting image quality. In this paper, using the characteristics of the side-slither data, we propose side-slither data-based vignetting correction of a high-resolution spaceborne camera with an optical focal plane assembly. First, the raw side-slither data standardization is used to ensure that each row has the same features. Then, with the spatial correlation of a gray-level co-occurrence matrix, the gray-level co-occurrence matrix is proposed to identify the uniform regions, to extract the sample points. Finally, due to the characteristics of compatible linear response and non-linear response, the power-law model was used to fit, and the Levenberg–Marquardt algorithm was used to fit the model. In the experiment, polynomial fitting, laboratory coefficients and on-orbit coefficients were used for comparison with the proposed method. The side-slither data can be treated as a uniform scene due to their characteristics, and the side-slither image that was corrected using the proposed method showed less than 1% change in mean value, a root-mean-square deviation value better than 0.1%, and the average streaking metrics were superior to 0.02. The results showed that the proposed method performs significantly better in the vignetting area. Full article

The scanning infrared focal plane array (IRFPA) suffers from stripe-like non-uniformity due to the usage of many detectors, especially when working with a large time scale. Typical calibration systems tend to block the sensor aperture and expose the detectors to an on-board blackbody calibration source. They may also point at deep space. Full aperture calibration sources of this type tend to be large and expensive. To address these problems, a dynamic non-uniformity correction (NUC) method is proposed based on a modulated internal calibration device. By employing the on-board calibration device to generate a dynamic scene and fully integrating the system characteristics of the scanning IRFPA into the scene-based non-uniformity correction (SBNUC) algorithm, on-orbit high dynamic range NUC is achieved without blocking the field of view. Here we simulate an internal calibration system alternative, where a dynamic calibration signal is superimposed on the normal imagery, thus requiring no mechanisms and a smaller size. This method using this type of calibrator shows that when the sensor is pointing at deep space for calibration, it provides an effective non-uniformity correction of the imagery. After performing the proposed method, the NU of the two evaluation images was reduced from the initial 12.99% and 8.72% to less than 2%. Compared to other on-board NUC methods that require an extended reference blackbody source, this proposed approach has the advantages of miniaturization, a short calibration time, and strong adaptability. Full article

Many existing scene-adaptive nonuniformity correction (NUC) methods suffer from slow convergence rate together with ghosting effects. In this paper, an improved NUC algorithm based on total variation penalized neural network regression is presented. Our work mainly focuses on solving the overfitting problem in least mean square (LMS) regression of traditional neural network NUC methods, which is realized by employing a total variation penalty in the cost function and redesigning the processing architecture. Moreover, an adaptive gated learning rate is presented to further reduce the ghosting artifacts and guarantee fast convergence. The performance of the proposed algorithm is comprehensively investigated with artificially corrupted test sequences and real infrared image sequences, respectively. Experimental results show that the proposed algorithm can effectively accelerate the convergence speed, suppress ghosting artifacts, and promote correction precision. Full article