Image Quality Enhancement Method Research Articles

Image Recognition System for Mechanical Parts Based on SSD and YOLOv5

With the development of industrial automation, the importance of mechanical parts in the production process is becoming increasingly important. In recent years, deep learning technology has made breakthrough progress in fields such as image recognition and object detection. Deep learning models can learn a large amount of data through multiple epochs, automatically extract features from it, and achieve image recognition and object detection. This provides potent technical assistance for the design of mechanical component recognition systems. In industrial production, there are a wide variety of mechanical parts with complex shapes, and the differences between different models are often very small, making it difficult to identify them manually. Therefore, it is necessary to develop a system that can automatically identify mechanical parts to improve production efficiency and quality. This article takes mechanical parts as the research object and studies the recognition technology of mechanical parts based on deep learning and image recognition. The main content of the article includes the following aspects. Research was conducted on image quality enhancement methods for images with different characteristics, and different image preprocessing methods were applied to different images. YOLOv5 and SSD (Single Shot MultiBox Detector) algorithms are used to perform deep learning and recognition detection on the processed images, respectively. By comprehensively comparing the differences between the two algorithms, YOLOv5 was selected as the focus of this design, and based on this, deep training was conducted, and the code was imported into the embedded system.

A Phase Recovery Technique Using the Genetic Algorithm for Aberration Correction in a Coherent Imaging System.

For traditional imaging systems, high imaging quality and system miniaturization are often contradictory. In order to meet the requirements of high imaging quality and system miniaturization, this paper proposes a method to correct the aberration of coherent imaging optical systems. The method is based on the idea of phase recovery and the imaging principle of a coherent imaging system to recover the aberrations at the exit pupil of the system. According to the recovered aberrations, conjugate filters are constructed to correct the image quality in the frequency domain. The imaging quality of the system is improved without changing the original optical path, and the simplicity of the system is guaranteed. To solve the pupil frequency domain aberration more accurately, this paper adopts the dual competition and parallel recombination strategy based on the genetic algorithm and introduces the disaster model. The improved genetic algorithm can effectively restrain the appearance of the "precocity" phenomenon. Finally, the paraxial imaging optical path is simulated and verified by experiments. The results show that, after aberration correction, the image sharpness is improved and the edge information is richer, which verifies the feasibility of the coherent imaging system image quality enhancement method proposed in this paper.

Research on an unsupervised person re-identification based on image quality enhancement method

Research on person re-identification(Re-ID) has important value in pedestrian detection, target tracking, criminal investigation, and other related fields. In unsupervised pedestrian recognition algorithms, the accuracy of pseudo-labels is crucial to the recognition results. However, in practical scenarios, low-quality images caused by factors such as differences in camera resolution and shooting angles can affect the extraction of pedestrian features by these algorithms, thereby negatively impacting the accuracy of the labels and the learning process of the model. To address this problem, we propose an image quality enhancement algorithm for unsupervised person Re-ID (IQE). To the best of our knowledge, this study is the first to introduce detail enhancement and the application of low-light enhancement algorithms into unsupervised person Re-ID. By improving the feature extraction quality based on these two aspects, higher-quality pseudo-labels can be constructed. This method improves the accuracy of feature extraction and clustering, thereby increasing the accuracy of pseudo-labels and reducing the interference of noisy pseudo-labels. The experimental results showed that the IQE method outperformed state-of-the-art person Re-ID methods in terms of Rank-1 accuracy and mAP. Specifically, IQE achieved an 87.9% rank-1 accuracy and a 71.2% mAP on the Market-1501 dataset; a 78.1% rank-1 accuracy and a 61.7% mAP On the DukeMTMC-reID dataset; and a 51.1% rank-1 accuracy and 24.2% mAP on the MSMT17 dataset.

Off-Site Distortion and Color Compensation of Underwater Archaeological Images Photographed in the Very Turbid Yellow Sea

Underwater photographing and image recording are essential for pre-excavation survey and during excavation in underwater archaeology. Unlike photographing on land, all underwater images suffer various quality degradations such as shape distortions, color shift, blur, low contrast, high noise levels and so on. Outcome is very often heavily photographing equipment and photographer dependent. Excavation schedule, weather conditions, and water conditions can put burdens on divers. Usable images are very limited compared to the efforts. In underwater archaeological study in very turbid water such as in the Yellow Sea (between mainland China and the Korean peninsula), underwater photographing is very challenging. In this study, off-site image distortion and color compensation techniques using an image processing/analysis software is investigated as an alternative image quality enhancement method. As sample images, photographs taken during the excavation of 800-year-old Taean Mado Shipwrecks in the Yellow Sea in 2008-2010 were mainly used. Significant enhancement in distortion and color compensation of archived images were obtained by simple post image processing using image processing/analysis software (PicMan) customized for given view ports, lenses and cameras with and without optical axis offsets. Post image processing is found to be very effective in distortion and color compensation of both recent and archived images from various photographing equipment models and configurations. Merits and demerit of in-situ, distortion and color compensated photographing with sophisticated equipment and conventional photographing equipment, which requires post image processing, are compared.

Combination of variational mode decomposition and coherent factor for ultrasound computer tomography.

BACKGROUND: Ultrasound computed tomography (USCT) is a promising technique for improving the detection of breast cancer. Image quality of USCT has a major impact on the breast cancer diagnosis.OBJECTIVE: This paper investigates the combination of variational mode decomposition (VMD) and coherent factor method for USCT image quality enhancement.METHODS: The signals can be decomposed into multiple intrinsic mode functions (IMFs) sifting through the frequency by VMD method. Refactoring the remaining IMFs, spatio-temporally smoothed coherence factor (STSCF) beamforming method is applied to reconstructed data for USCT.RESULTS: The validation of combination the VMD and STSCF is described through the breast phantom experiment and in vivo experiments. The evaluation indicators such as contrast ratio (CR), contrast to noise ratio (CNR) and signal to noise ratio (SNR) have been better improved in the experimental results. For the breast phantom, the proposed method gives a higher resolution and the better contrast properties for the hyperechoic cyst. The borders of cysts and tumors in the breast phantom can be distinguished clearly. For volunteer breast experiments, artifacts are removed more efficiently while the clutters are suppressed simultaneously.CONCLUSION: The combination of VMD and STSCF can further reduce the noise and suppress the side lobes.

Integrating Image Quality Enhancement Methods and Deep Learning Techniques for Remote Sensing Scene Classification

Through the continued development of technology, applying deep learning to remote sensing scene classification tasks is quite mature. The keys to effective deep learning model training are model architecture, training strategies, and image quality. From previous studies of the author using explainable artificial intelligence (XAI), image cases that have been incorrectly classified can be improved when the model has adequate capacity to correct the classification after manual image quality correction; however, the manual image quality correction process takes a significant amount of time. Therefore, this research integrates technologies such as noise reduction, sharpening, partial color area equalization, and color channel adjustment to evaluate a set of automated strategies for enhancing image quality. These methods can enhance details, light and shadow, color, and other image features, which are beneficial for extracting image features from the deep learning model to further improve the classification efficiency. In this study, we demonstrate that the proposed image quality enhancement strategy and deep learning techniques can effectively improve the scene classification performance of remote sensing images and outperform previous state-of-the-art approaches.

Perceptual Image Enhancement by Relativistic Discriminant Learning With Cross-Scale Aggregated Representation

In this paper, we introduce an automatic method for image quality enhancement. Our method relies on a multi-level connected generator and a pair-wise relativistic discriminator. Moreover, we also incorporate auxiliary loss functions to exhibit a high-quality image enhancement. Different from other image style transfer approaches, our method has two appealing properties: 1) different scale information not only pass on the same scale but also flow from encoder to decoder freely and 2) more contextual information is provided to help the discriminator to perform pair-wise discriminative learning. The learned generative model can then automatically enhance the low-quality image into a digital single lens reflex (DSLR) quality. The extensive experiments demonstrate that the proposed framework surpasses the state-of-the-art methods with a clear margin.

Image Quality Enhancement Method using Retinex in HSV Color Space and Saturation Correction

Image Quality Enhancement Method using Retinex in HSV Color Space and Saturation Correction

New Method for Large-Area Satellite Image Quality Enhancement with Local Aerial Multi-Point Calibration

New Method for Large-Area Satellite Image Quality Enhancement with Local Aerial Multi-Point Calibration

Three-dimensional wavelets-based denoising of hyperspectral imagery

We propose a three-dimensional (3-D) denoising approach and coding scheme. The suggested denoising algorithm is taking full advantage of the supplied volumetric data by decomposing the original hyperspectral imagery into individual subspaces, applying an orthogonal isotropic 3-D divergence-free wavelet transformation. The delineated capability of hierarchically structured wavelet coefficients improves the efficiency of the suggested denoising algorithm and effectively preserves the finest details and the relevant image features by emphasizing a nonlocal similarity and spectral-spatial structure of hyperspectral imagery into sparse representation. The proposed method is evaluated using spectral angle distance for a ground-truth spectral dataset and by classification accuracies using water quality indices, which are particularly sensitive to the presence of noise. The reported results are based on a real dataset, presenting three different airborne hyperspectral systems: AHS, CASI-1500i, and AisaEAGLE. Several qualitative and quantitative evaluation measures are applied to validate the ability of the suggested method for noise reduction and image quality enhancement. Experimental results demonstrate that the proposed denoising algorithm achieves better performance when applied on the suggested wavelet transformation compared with other examined noise reduction and hyperspectral image restoration techniques.

Method for Large-Area Satellite Image Quality Enhancement With Local Aerial Images Based on Non-Target Multi-Point Calibration

This paper designed a non-target multi-point calibration method for the quality enhancement of large-area satellite images by using local aerial images. Satellite images are more sensitive to atmospheric effects compared with aerial images. Atmospheric effects on aerial images are even negligible in fine weather. Given that aerial remote sensing has high spatial resolution and geometric fidelity, more spatial details can be recorded in aerial images. However, the scan bandwidth of aerial images is limited compared with that of satellite images. Thus, taking high-quality aerial images of a neighborhood as reference can provide prior knowledge for point spread function (PSF) estimation and for the quality enhancement of large-area satellite images. The least square method and interpolation are used for the PSF estimation of spatial variation, and then total variation minimization is used for recovery. The results show that the designed method can effectively enhance the quality of large-area satellite images.

ROV-based Underwater Vision System for Intelligent Fish Ethology Research

Fish ethology is a prospective discipline for ocean surveys. In this paper, one ROV-based system is established to perform underwater visual tasks with customized optical sensors installed. One image quality enhancement method is first presented in the context of creating underwater imaging models combined with homomorphic filtering and wavelet decomposition. The underwater vision system can further detect and track swimming fish from the resulting images with the strategies developed using curve evolution and particular filtering, in order to obtain a deeper understanding of fish behaviours. The simulation results have shown the excellent performance of the developed scheme, in regard to both robustness and effectiveness.

Paper No 14.2: Image Quality Enhancement for Mobile Display Under Outdoor Environment

AbstractThis paper presents outdoor image quality enhancement methods based on adjusting gamma shape and chroma compensation for mobile display. In order to verify the suggested method, a psychophysical experiment was performed using 22 test images that are categorised by colour attributes and contents. The test images that are applied both methods are more preferred than others with statistical significant difference.

Filtering Techniques to reduce Speckle Noise and Image Quality Enhancement methods on Satellite Images

Noise in images is the vital factor which degrades the quality of the images. Reducing noise from the satellite images, medical images etc., is a challenge for the researchers in digital image processing. Several approaches are there for noise reduction. Generally speckle noise is commonly found in Synthetic Aperture Radar (SAR) satellite images and medical images. This research paper put forward some of the filtering techniques for the removal of speckle noise from the satellite images, which enhances the quality of the images. Although many filters are available for speckle reduction, some filters are best suited for SAR images are used for which the statistical parameters are calculated for the output images obtained from all the filters. The statistical measures SNR, PSNR, RMSE and CoC are compared. The output images corresponding to the best statistical values are displayed along with the filters name and corresponding values of the statistical measures.

Image Quality Enhancement Using the Direction and Thickness of Vein Lines for Finger-Vein Recognition

On the basis of the increased emphasis placed on the protection of privacy, biometric recognition systems using physical or behavioural characteristics such as fingerprints, facial characteristics, iris and finger-vein patterns or the voice have been introduced in applications including door access control, personal certification, Internet banking and ATM machines. Among these, finger-vein recognition is advantageous in that it involves the use of inexpensive and small devices that are difficult to counterfeit. In general, finger-vein recognition systems capture images by using near infrared (NIR) illumination in conjunction with a camera. However, such systems can face operational difficulties, since the scattering of light from the skin can make capturing a clear image difficult. To solve this problem, we proposed new image quality enhancement method that measures the direction and thickness of vein lines. This effort represents novel research in four respects. First, since vein lines are detected in input images based on eight directional profiles of a grey image instead of binarized images, the detection error owing to the non-uniform illumination of the finger area can be reduced. Second, our method adaptively determines a Gabor filter for the optimal direction and width on the basis of the estimated direction and thickness of a detected vein line. Third, by applying this optimized Gabor filter, a clear vein image can be obtained. Finally, the further processing of the morphological operation is applied in the Gabor filtered image and the resulting image is combined with the original one, through which finger-vein image of a higher quality is obtained. Experimental results from application of our proposed image enhancement method show that the equal error rate (EER) of finger-vein recognition decreases to approximately 0.4% in the case of a local binary pattern-based recognition and to approximately 0.3% in the case of a wavelet transform-based recognition.

Detection Devices and Technologies on Large-Scale Pipe Weld Surface Defect

The kind and extent of defect can be identified through image processing. First, the weld defect detection device should be constructed, and then the defect imaged should be obtained through rational way, in order to enhance the image quality, image filter and image enhancement method should be use. To ensure the real-time system, the weld region need to segment from the image. After that, the needed defect features need to determine and extract. Finally, the kind, the location and the size of the defect can be defined.

P‐51: A Novel Image Quality Enhancement Method for AMOLEDs

AbstractWe have developed a novel image quality enhancement method for AMOLEDs, which is based on advanced sunlight readability (ASR) technology. It consists of adaptive gamma peak control (AGPC) image processing algorithm and the reflectance reduction method (RRM). AGPC can boost the peak luminance of some specific images up to 2 times higher than that of normal driving in AMOLEDs while the maximum power consumption can be reduced by 40% depending on image contents. RRM can achieve half of reflectance from panel surface to cover of mobile device in applying optical plastic. The developed method is useful for acquiring more vivid image and better readability under sunlight condition.

39.4: Inverse of Mapping Function (IMF) Method for Image Quality Enhancement of High Dynamic Range LCD TVs

AbstractA high dynamic range liquid crystal display (HDR‐LCD) can much enhance the contrast ratio of the image on a locally controlled dynamic backlight. A novel method, inverse of mapping function (IMF), was demonstrated to further improve the HDR image quality from the information of each frame. By using the IMF method, the image contrast ratio in a 37″ HDR‐LCD TV was successfully improved to ∼20,000:1 while maintaining high brightness and clear image details. Besides, for mass production purpose, the backlight of a 37″ HDR‐LCD TV was divide into 8×8 zones to reduce hardware computation complexity.